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Ryan EG, Gao CX, Grantham KL, Thao LTP, Charles-Nelson A, Bowden R, Herschtal A, Lee KJ, Forbes AB, Heritier S, Phillipou A, Wolfe R. Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research. Int J Eat Disord 2024. [PMID: 38469971 DOI: 10.1002/eat.24187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.
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Affiliation(s)
- Elizabeth G Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Caroline X Gao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
| | - Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Le Thi Phuong Thao
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anaïs Charles-Nelson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Alan Herschtal
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Katherine J Lee
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrea Phillipou
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- Department of Psychological Sciences, Swinburne University of Technology, Melbourne, Victoria, Australia
- Department of Mental Health, Austin Health, Melbourne, Victoria, Australia
- Department of Mental Health, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Rory Wolfe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Korevaar E, Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study. BMC Med Res Methodol 2024; 24:31. [PMID: 38341540 PMCID: PMC10858609 DOI: 10.1186/s12874-024-02147-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/10/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data. METHODS We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods. RESULTS Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method. CONCLUSIONS Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Amalia Karahalios
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.
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Grantham KL, Forbes AB, Hooper R, Kasza J. The staircase cluster randomised trial design: A pragmatic alternative to the stepped wedge. Stat Methods Med Res 2024; 33:24-41. [PMID: 38031417 PMCID: PMC10863363 DOI: 10.1177/09622802231202364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
This article introduces the 'staircase' design, derived from the zigzag pattern of steps along the diagonal of a stepped wedge design schematic where clusters switch from control to intervention conditions. Unlike a complete stepped wedge design where all participating clusters must collect and provide data for the entire trial duration, clusters in a staircase design are only required to be involved and collect data for a limited number of pre- and post-switch periods. This could alleviate some of the burden on participating clusters, encouraging involvement in the trial and reducing the likelihood of attrition. Staircase designs are already being implemented, although in the absence of a dedicated methodology, approaches to sample size and power calculations have been inconsistent. We provide expressions for the variance of the treatment effect estimator when a linear mixed model for an outcome is assumed for the analysis of staircase designs in order to enable appropriate sample size and power calculations. These include explicit variance expressions for basic staircase designs with one pre- and one post-switch measurement period. We show how the variance of the treatment effect estimator is related to key design parameters and demonstrate power calculations for examples based on a real trial.
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Affiliation(s)
- Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Richard Hooper
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Korevaar E, Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study. Res Synth Methods 2023; 14:882-902. [PMID: 37731166 PMCID: PMC10946504 DOI: 10.1002/jrsm.1669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 08/11/2023] [Accepted: 08/29/2023] [Indexed: 09/22/2023]
Abstract
Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Simon L. Turner
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Andrew B. Forbes
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Amalia Karahalios
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Monica Taljaard
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaOntarioCanada
- School of Epidemiology and Public HealthUniversity of OttawaOttawaOntarioCanada
| | - Joanne E. McKenzie
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
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Kasza J, Bowden R, Ouyang Y, Taljaard M, Forbes AB. Does it decay? Obtaining decaying correlation parameter values from previously analysed cluster randomised trials. Stat Methods Med Res 2023; 32:2123-2134. [PMID: 37589088 PMCID: PMC10683336 DOI: 10.1177/09622802231194753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
A frequently applied assumption in the analysis of data from cluster randomised trials is that the outcomes from all participants within a cluster are equally correlated. That is, the intracluster correlation, which describes the degree of dependence between outcomes from participants in the same cluster, is the same for each pair of participants in a cluster. However, recent work has discussed the importance of allowing for this correlation to decay as the time between the measurement of participants in a cluster increases. Incorrect omission of such a decay can lead to under-powered studies, and confidence intervals for estimated treatment effects can be too narrow or too wide, depending on the characteristics of the design. When planning studies, researchers often rely on previously reported analyses of trials to inform their choice of intracluster correlation. However, most reported analyses of clustered data do not incorporate a correlation decay. Thus, often all that is available are estimates of intracluster correlations obtained under the potentially incorrect assumption of no decay. In this article, we show that it is possible to use intracluster correlation values obtained from models that incorrectly omit a decay to inform plausible choices of decaying correlations. Our focus is on intracluster correlation estimates for continuous outcomes obtained by fitting linear mixed models with exchangeable or block-exchangeable correlation structures. We describe how plausible values for decaying correlations may be obtained given these estimated intracluster correlations. An online app is presented that allows users to obtain plausible values of the decay, which can be used at the trial planning stage to assess the sensitivity of sample size and power calculations to decaying correlation structures.
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Affiliation(s)
- Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Yongdong Ouyang
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Li F, Kasza J, Turner EL, Rathouz PJ, Forbes AB, Preisser JS. Generalizing the information content for stepped wedge designs: A marginal modeling approach. Scand Stat Theory Appl 2023; 50:1048-1067. [PMID: 37601275 PMCID: PMC10434823 DOI: 10.1111/sjos.12615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 09/02/2022] [Indexed: 11/30/2022]
Abstract
Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance-stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.
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Affiliation(s)
- Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Paul J. Rathouz
- Department of Population Health, The University of Texas at Austin, Austin, Texas, USA
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - John S. Preisser
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Gruen RL, Mitra B, Bernard SA, McArthur CJ, Burns B, Gantner DC, Maegele M, Cameron PA, Dicker B, Forbes AB, Hurford S, Martin CA, Mazur SM, Medcalf RL, Murray LJ, Myles PS, Ng SJ, Pitt V, Rashford S, Reade MC, Swain AH, Trapani T, Young PJ. Prehospital Tranexamic Acid for Severe Trauma. N Engl J Med 2023; 389:127-136. [PMID: 37314244 DOI: 10.1056/nejmoa2215457] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Whether prehospital administration of tranexamic acid increases the likelihood of survival with a favorable functional outcome among patients with major trauma and suspected trauma-induced coagulopathy who are being treated in advanced trauma systems is uncertain. METHODS We randomly assigned adults with major trauma who were at risk for trauma-induced coagulopathy to receive tranexamic acid (administered intravenously as a bolus dose of 1 g before hospital admission, followed by a 1-g infusion over a period of 8 hours after arrival at the hospital) or matched placebo. The primary outcome was survival with a favorable functional outcome at 6 months after injury, as assessed with the use of the Glasgow Outcome Scale-Extended (GOS-E). Levels on the GOS-E range from 1 (death) to 8 ("upper good recovery" [no injury-related problems]). We defined survival with a favorable functional outcome as a GOS-E level of 5 ("lower moderate disability") or higher. Secondary outcomes included death from any cause within 28 days and within 6 months after injury. RESULTS A total of 1310 patients were recruited by 15 emergency medical services in Australia, New Zealand, and Germany. Of these patients, 661 were assigned to receive tranexamic acid, and 646 were assigned to receive placebo; the trial-group assignment was unknown for 3 patients. Survival with a favorable functional outcome at 6 months occurred in 307 of 572 patients (53.7%) in the tranexamic acid group and in 299 of 559 (53.5%) in the placebo group (risk ratio, 1.00; 95% confidence interval [CI], 0.90 to 1.12; P = 0.95). At 28 days after injury, 113 of 653 patients (17.3%) in the tranexamic acid group and 139 of 637 (21.8%) in the placebo group had died (risk ratio, 0.79; 95% CI, 0.63 to 0.99). By 6 months, 123 of 648 patients (19.0%) in the tranexamic acid group and 144 of 629 (22.9%) in the placebo group had died (risk ratio, 0.83; 95% CI, 0.67 to 1.03). The number of serious adverse events, including vascular occlusive events, did not differ meaningfully between the groups. CONCLUSIONS Among adults with major trauma and suspected trauma-induced coagulopathy who were being treated in advanced trauma systems, prehospital administration of tranexamic acid followed by an infusion over 8 hours did not result in a greater number of patients surviving with a favorable functional outcome at 6 months than placebo. (Funded by the Australian National Health and Medical Research Council and others; PATCH-Trauma ClinicalTrials.gov number, NCT02187120.).
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Affiliation(s)
- Russell L Gruen
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Biswadev Mitra
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Stephen A Bernard
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Colin J McArthur
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Brian Burns
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Dashiell C Gantner
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Marc Maegele
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Peter A Cameron
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Bridget Dicker
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Andrew B Forbes
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Sally Hurford
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Catherine A Martin
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Stefan M Mazur
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Robert L Medcalf
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Lynnette J Murray
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Paul S Myles
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Sze J Ng
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Veronica Pitt
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Stephen Rashford
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Michael C Reade
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Andrew H Swain
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Tony Trapani
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
| | - Paul J Young
- From the College of Health and Medicine, Australian National University (R.L.G.), Canberra Health Services (R.L.G.), and Joint Health Command, Australian Defence Force (M.C.R.), Canberra, ACT, the Emergency and Trauma Centre (B.M., P.A.C.) and the Departments of Anaesthesiology and Perioperative Medicine (P.S.M.) and Intensive Care (S.A.B., D.C.G.), Alfred Hospital, the Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine (D.C.G., L.J.M., S.J.N., T.T., P.J.Y.), the School of Public Health and Preventive Medicine (B.M., S.A.B., C.J.M., P.A.C., A.B.F., C.A.M., V.P.), the Australian Centre for Blood Diseases (R.L.M.), and the Central Clinical School (P.S.M.), Monash University, Ambulance Victoria (S.A.B.), and the Department of Critical Care, University of Melbourne (P.J.Y.), Melbourne, Aeromedical Operations, NSW Ambulance, Trauma Service, Royal North Shore Hospital, and Sydney Medical School, University of Sydney, Sydney (B.B.), MedSTAR Emergency Medical Retrieval Services, South Australian Ambulance Service (S.M.M.), and the Emergency Department, Royal Adelaide Hospital (S.M.M.), Adelaide, SA, and Queensland Ambulance Service (S.R.) and the Faculty of Medicine, University of Queensland (M.C.R.), Brisbane - all in Australia; Te Toka Tumai Auckland City Hospital (C.J.M.), Hato Hone St. John, Mt. Wellington (B.D.), and the Department of Paramedicine, Faculty of Health and Environmental Sciences, Auckland University of Technology (B.D., A.H.S.), Auckland, and Medical Research Institute of New Zealand (C.J.M., S.H., P.J.Y.), Wellington Free Ambulance (A.H.S.), and the Intensive Care Unit, Wellington Hospital (P.J.Y.), Wellington - all in New Zealand; Cologne-Merheim Medical Center, Department of Traumatology, Orthopedic Surgery, and Sports Medicine, and the Institute for Research in Operative Medicine, Witten-Herdecke University - both in Cologne, Germany (M.M.)
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8
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Rezaei-Darzi E, Grantham KL, Forbes AB, Kasza J. The impact of iterative removal of low-information cluster-period cells from a stepped wedge design. BMC Med Res Methodol 2023; 23:160. [PMID: 37415140 PMCID: PMC10324156 DOI: 10.1186/s12874-023-01969-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 06/08/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Standard stepped wedge trials, where clusters switch from the control to the intervention condition in a staggered manner, can be costly and burdensome. Recent work has shown that the amount of information contributed by each cluster in each period differs, with some cluster-periods contributing a relatively small amount of information. We investigate the patterns of the information content of cluster-period cells upon iterative removal of low-information cells, assuming a model for continuous outcomes with constant cluster-period size, categorical time period effects, and exchangeable and discrete-time decay intracluster correlation structures. METHODS We sequentially remove pairs of "centrosymmetric" cluster-period cells from an initially complete stepped wedge design which contribute the least amount of information to the estimation of the treatment effect. At each iteration, we update the information content of the remaining cells, determine the pair of cells with the lowest information content, and repeat this process until the treatment effect cannot be estimated. RESULTS We demonstrate that as more cells are removed, more information is concentrated in the cells near the time of the treatment switch, and in "hot-spots" in the corners of the design. For the exchangeable correlation structure, removing the cells from these hot-spots leads to a marked reduction in study precision and power, however the impact of this is lessened for the discrete-time decay structure. CONCLUSIONS Removing cluster-period cells distant from the time of the treatment switch may not lead to large reductions in precision or power, implying that certain incomplete designs may be almost as powerful as complete designs.
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Affiliation(s)
- Ehsan Rezaei-Darzi
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Kelsey L. Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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9
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Turner SL, Korevaar E, Cumpston MS, Kanukula R, Forbes AB, McKenzie JE. Effect estimates can be accurately calculated with data digitally extracted from interrupted time series graphs. Res Synth Methods 2023; 14:622-638. [PMID: 37293884 PMCID: PMC10946754 DOI: 10.1002/jrsm.1646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/12/2023] [Accepted: 05/10/2023] [Indexed: 06/10/2023]
Abstract
Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide raw data for re-analysis, graphs are often included, from which time series data can be digitally extracted. However, the accuracy of effect estimates calculated from data digitally extracted from ITS graphs is currently unknown. Forty-three ITS with available datasets and time series graphs were included. Time series data from each graph was extracted by four researchers using digital data extraction software. Data extraction errors were analysed. Segmented linear regression models were fitted to the extracted and provided datasets, from which estimates of immediate level and slope change (and associated statistics) were calculated and compared across the datasets. Although there were some data extraction errors of time points, primarily due to complications in the original graphs, they did not translate into important differences in estimates of interruption effects (and associated statistics). Using digital data extraction to obtain data from ITS graphs should be considered in reviews including ITS. Including these studies in meta-analyses, even with slight inaccuracy, is likely to outweigh the loss of information from non-inclusion.
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Affiliation(s)
- Simon Lee Turner
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
| | - Elizabeth Korevaar
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
| | - Miranda S. Cumpston
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
| | - Raju Kanukula
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
| | - Andrew B. Forbes
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
| | - Joanne E. McKenzie
- School of Public Health and Preventive MedicineMonash UniversityMelbourneAustralia
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10
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Teerenstra S, Kasza J, Leontjevas R, Forbes AB. Sample size for partially nested designs and other nested or crossed designs with a continuous outcome when adjusted for baseline. Stat Med 2023. [PMID: 37348855 DOI: 10.1002/sim.9820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 04/28/2023] [Accepted: 05/31/2023] [Indexed: 06/24/2023]
Abstract
In a randomized controlled trial, outcomes of different subjects may be independent at baseline, but correlated at a follow-up measurement due to treatment. This treatment-related clustering at follow-up can arise for instance because the treatment is given in a group or because subjects are treated individually but by the same therapist (therapist effect). There is substantial literature on the design and analysis of such trials when estimation of the intervention effect is based on a follow-up measurement (eg, directly after treatment or at a later time point). However, often the baseline measurement of the outcome is highly correlated with the follow-up measurement, and this information can be used in the analysis. For a randomized design with a baseline and a follow-up measurement, we compare sample size requirements for analyses with and without adjustment for this baseline measure. We show that adjusting for baseline reduces required sample size. This reduction depends on the variance of the difference between arms at baseline, the variance of this difference at follow-up, and the correlation between the two. From this, we derive sample size formulas for partially or fully nested designs, and cluster randomized trials with treatment as a partially or fully cross-classified factor. Also, we discuss situations where clusters are already present at baseline or where treatment by cluster interaction is present. For the partially nested design, we work out practical design considerations (eg, use of content-matter input, design factors and optimal allocation ratio) and investigate small sample properties of the sample size formula.
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Affiliation(s)
- Steven Teerenstra
- Department for Health Evidence, Section Biostatistics, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Ruslan Leontjevas
- Department of Primary and Community Care, Radboud University, Medical Center, Nijmegen, The Netherlands
- Faculty of Psychology and Educational Sciences, Open University of The Netherlands, Heerlen, The Netherlands
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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11
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McGuinness SL, Eades O, Grantham KL, Zhong S, Johnson J, Cameron PA, Forbes AB, Fisher JR, Hodgson CL, Kasza J, Kelsall H, Kirkman M, Russell GM, Russo PL, Sim MR, Singh K, Skouteris H, Smith K, Stuart RL, Trauer JM, Udy A, Zoungas S, Leder K. Mental health and wellbeing of health and aged care workers in Australia, May 2021 - June 2022: a longitudinal cohort study. Med J Aust 2023; 218:361-367. [PMID: 37032118 DOI: 10.5694/mja2.51918] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 11/28/2022] [Accepted: 12/19/2022] [Indexed: 04/11/2023]
Abstract
OBJECTIVES To assess the mental health and wellbeing of health and aged care workers in Australia during the second and third years of the coronavirus disease 2019 (COVID-19) pandemic, overall and by occupation group. DESIGN, SETTING, PARTICIPANTS Longitudinal cohort study of health and aged care workers (ambulance, hospitals, primary care, residential aged care) in Victoria: May-July 2021 (survey 1), October-December 2021 (survey 2), and May-June 2022 (survey 3). MAIN OUTCOME MEASURES Proportions of respondents (adjusted for age, gender, socio-economic status) reporting moderate to severe symptoms of depression (Patient Health Questionnaire-9, PHQ-9), anxiety (Generalized Anxiety Disorder scale, GAD-7), or post-traumatic stress (Impact of Event Scale-6, IES-6), burnout (abbreviated Maslach Burnout Inventory, aMBI), or high optimism (10-point visual analogue scale); mean scores (adjusted for age, gender, socio-economic status) for wellbeing (Personal Wellbeing Index-Adult, PWI-A) and resilience (Connor Davidson Resilience Scale 2, CD-RISC-2). RESULTS A total of 1667 people responded to at least one survey (survey 1, 989; survey 2, 1153; survey 3, 993; response rate, 3.3%). Overall, 1211 survey responses were from women (72.6%); most respondents were hospital workers (1289, 77.3%) or ambulance staff (315, 18.9%). The adjusted proportions of respondents who reported moderate to severe symptoms of depression (survey 1, 16.4%; survey 2, 22.6%; survey 3, 19.2%), anxiety (survey 1, 8.8%; survey 2, 16.0%; survey 3, 11.0%), or post-traumatic stress (survey 1, 14.6%; survey 2, 35.1%; survey 3, 14.9%) were each largest for survey 2. The adjusted proportions of participants who reported moderate to severe symptoms of burnout were higher in surveys 2 and 3 than in survey 1, and the proportions who reported high optimism were smaller in surveys 2 and 3 than in survey 1. Adjusted mean scores for wellbeing and resilience were similar at surveys 2 and 3 and lower than at survey 1. The magnitude but not the patterns of change differed by occupation group. CONCLUSION Burnout was more frequently reported and mean wellbeing and resilience scores were lower in mid-2022 than in mid-2021 for Victorian health and aged care workers who participated in our study. Evidence-based mental health and wellbeing programs for workers in health care organisations are needed. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry: ACTRN12621000533897 (observational study; retrospective).
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Affiliation(s)
| | - Owen Eades
- Alfred Health, Melbourne, VIC
- Monash University, Melbourne, VIC
| | | | - Shannon Zhong
- Alfred Health, Melbourne, VIC
- Monash University, Melbourne, VIC
| | - Josphin Johnson
- Alfred Health, Melbourne, VIC
- Monash University, Melbourne, VIC
| | - Peter A Cameron
- Monash University, Melbourne, VIC
- The Alfred Emergency and Trauma Centre, Alfred Health, Melbourne, VIC
| | | | | | - Carol L Hodgson
- Alfred Health, Melbourne, VIC
- Monash University, Melbourne, VIC
| | | | | | | | | | - Philip L Russo
- Monash University, Melbourne, VIC
- Cabrini Health, Melbourne, VIC
| | | | - Kasha Singh
- The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC
- Peninsula Health, Melbourne, VIC
| | - Helen Skouteris
- Monash Centre for Health Research and Implementation, Monash University, Melbourne, VIC
| | - Karen Smith
- Monash University, Melbourne, VIC
- Ambulance Service of Victoria, Melbourne, VIC
| | - Rhonda L Stuart
- Monash University, Melbourne, VIC
- Monash Health, Melbourne, VIC
| | | | - Andrew Udy
- Alfred Health, Melbourne, VIC
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, VIC
| | | | - Karin Leder
- Monash University, Melbourne, VIC
- Royal Melbourne Hospital, Melbourne, VIC
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12
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Lim YZ, Cicuttini FM, Wluka AE, Jones G, Hill CL, Forbes AB, Tonkin A, Berezovskaya S, Tan L, Ding C, Wang Y. Effect of atorvastatin on skeletal muscles of patients with knee osteoarthritis: Post-hoc analysis of a randomised controlled trial. Front Med (Lausanne) 2022; 9:939800. [PMID: 36091679 PMCID: PMC9452814 DOI: 10.3389/fmed.2022.939800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/05/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Populations with knee osteoarthritis (KOA) are at increased risk of cardiovascular disease, due to higher prevalence of risk factors including dyslipidaemia, where statins are commonly prescribed. However, the effect of statins on muscles and symptoms in this population is unknown. Thus, this study examined the effect of atorvastatin on muscle properties in patients with symptomatic KOA. Design Post-hoc analysis of a 2-year multicentre randomised, double-blind, placebo-controlled trial. Setting Australian community. Participants Participants aged 40–70 years (mean age 55.7 years, 55.6% female) with KOA who met the American College of Rheumatology clinical criteria received atorvastatin 40 mg daily (n = 151) or placebo (n = 153). Main outcome measures Levels of creatinine kinase (CK), aspartate transaminase (AST), and alanine transaminase (ALT) at 1, 6, 12, and 24 months; muscle strength (by dynamometry) at 12 and 24 months; vastus medialis cross-sectional area (CSA) on magnetic resonance imaging at 24 months; and self-reported myalgia. Results There were no significant between-group differences in CK and AST at all timespoints. The atorvastatin group had higher ALT than placebo group at 1 (median 26 vs. 21, p = 0.004) and 6 (25 vs. 22, p = 0.007) months without significant between-group differences at 12 and 24 months. Muscle strength increased in both groups at 24 months without between-group differences [mean 8.2 (95% CI 3.5, 12.9) vs. 5.9 (1.3, 10.4), p = 0.49]. Change in vastus medialis CSA at 24 months favoured the atorvastatin group [0.11 (−0.10, 0.31) vs. −0.23 (−0.43, −0.03), p = 0.02] but of uncertain clinical significance. There was a trend for more myalgia in the atorvastatin group (8/151 vs. 2/153, p = 0.06) over 2 years, mostly occurring within 6 months (7/151 vs. 1/153, p = 0.04). Conclusions In those with symptomatic KOA, despite a trend for more myalgia, there was no clear evidence of an adverse effect of atorvastatin on muscles, including those most relevant to knee joint health.
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Affiliation(s)
- Yuan Z. Lim
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Flavia M. Cicuttini
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Anita E. Wluka
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Graeme Jones
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Catherine L. Hill
- The Queen Elizabeth Hospital, University of Adelaide, Woodville, SA, Australia
- Department of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Andrew Tonkin
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Sofia Berezovskaya
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lynn Tan
- Alfred Hospital, Melbourne, VIC, Australia
| | - Changhai Ding
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yuanyuan Wang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- *Correspondence: Yuanyuan Wang,
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13
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Kasza J, Bowden R, Hooper R, Forbes AB. The batched stepped wedge design: A design robust to delays in cluster recruitment. Stat Med 2022; 41:3627-3641. [PMID: 35596691 PMCID: PMC9541502 DOI: 10.1002/sim.9438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 04/13/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022]
Abstract
Stepped wedge designs are an increasingly popular variant of longitudinal cluster randomized trial designs, and roll out interventions across clusters in a randomized, but step-wise fashion. In the standard stepped wedge design, assumptions regarding the effect of time on outcomes may require that all clusters start and end trial participation at the same time. This would require ethics approvals and data collection procedures to be in place in all clusters before a stepped wedge trial can start in any cluster. Hence, although stepped wedge designs are useful for testing the impacts of many cluster-based interventions on outcomes, there can be lengthy delays before a trial can commence. In this article, we introduce "batched" stepped wedge designs. Batched stepped wedge designs allow clusters to commence the study in batches, instead of all at once, allowing for staggered cluster recruitment. Like the stepped wedge, the batched stepped wedge rolls out the intervention to all clusters in a randomized and step-wise fashion: a series of self-contained stepped wedge designs. Provided that separate period effects are included for each batch, software for standard stepped wedge sample size calculations can be used. With this time parameterization, in many situations including when linear models are assumed, sample size calculations reduce to the setting of a single stepped wedge design with multiple clusters per sequence. In these situations, sample size calculations will not depend on the delays between the commencement of batches. Hence, the power of batched stepped wedge designs is robust to unexpected delays between batches.
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Affiliation(s)
- Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Richard Hooper
- Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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14
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Grantham KL, Kasza J, Heritier S, Carlin JB, Forbes AB. Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters. BMC Med Res Methodol 2022; 22:112. [PMID: 35418034 PMCID: PMC9009029 DOI: 10.1186/s12874-022-01550-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/02/2022] [Indexed: 11/25/2022] Open
Abstract
Background Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters. Small-sample methods such as the Kenward-Roger approximation in combination with REML can potentially improve estimation of the fixed effects such as the treatment effect. A Bayesian approach may also be promising for such multilevel models but has not yet seen much application in cluster randomized trials. Methods We conducted a simulation study comparing the performance of REML with and without a Kenward-Roger approximation to a Bayesian approach using weakly informative prior distributions on the intracluster correlation parameters. We considered a continuous outcome and a range of stepped wedge trial configurations with between 4 and 40 clusters. To assess method performance we calculated bias and mean squared error for the treatment effect and correlation parameters and the coverage of 95% confidence/credible intervals and relative percent error in model-based standard error for the treatment effect. Results Both REML with a Kenward-Roger standard error and degrees of freedom correction and the Bayesian method performed similarly well for the estimation of the treatment effect, while intracluster correlation parameter estimates obtained via the Bayesian method were less variable than REML estimates with different relative levels of bias. Conclusions The use of REML with a Kenward-Roger approximation may be sufficient for the analysis of stepped wedge cluster randomized trials with a small number of clusters. However, a Bayesian approach with weakly informative prior distributions on the intracluster correlation parameters offers a viable alternative, particularly when there is interest in the probability-based inferences permitted within this paradigm. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01550-8).
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Affiliation(s)
- Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - John B Carlin
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Carlton, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
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15
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Korevaar E, Karahalios A, Turner SL, Forbes AB, Taljaard M, Cheng AC, Grimshaw JM, Bero L, McKenzie JE. Methodological systematic review recommends improvements to conduct and reporting when meta-analysing interrupted time series studies. J Clin Epidemiol 2022; 145:55-69. [PMID: 35045318 DOI: 10.1016/j.jclinepi.2022.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/21/2021] [Accepted: 01/13/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVES Interrupted Time Series (ITS) are a type of non-randomised design commonly used to evaluate public health policy interventions, and the impact of exposures, at the population level. Meta-analysis may be used to combine results from ITS across studies (in the context of systematic reviews) or across sites within the same study. We aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyse results from ITS. STUDY DESIGN AND SETTINGS Eight electronic databases were searched to identify reviews (published 2000-2019) that meta-analysed at least two ITS. Characteristics of the included reviews, the statistical methods used to analyse the ITS and meta-analyse their results, effect measures, and risk of bias assessment tools were extracted. RESULTS Of the 4213 identified records, 54 reviews were included. Nearly all reviews (94%) used two-stage meta-analysis, most commonly fitting a random effects model (69%). Among the 41 reviews that re-analysed the ITS, linear regression (39%) and ARIMA (20%) were most commonly used; 38% adjusted for autocorrelation. The most common effect measure meta-analysed was an immediate level-change (46/54). Reporting of the statistical methods and ITS characteristics was often incomplete. CONCLUSION Improvement is needed in the conduct and reporting of reviews that meta-analyse results from ITS.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Amalia Karahalios
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, 3010, Victoria Australia
| | - Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, K1Y 4E9, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia; Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, K1Y 4E9, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, K1H 8M5, Canada
| | - Lisa Bero
- School of Medicine and Colorado School of Public Health, Center for Bioethics and Humanities, University of Colorado Anschutz Medical Campus, 13080 E. 19th Ave, Aurora, CO 80045
- Mail Stop B137, Denver, Colorado
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Victoria, Australia.
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16
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Urquhart DM, Rosenfeld JV, van Tulder M, Wluka AE, Leder K, Cheng AC, Forbes AB, Chan P, O'Sullivan R, Liew S, Cicuttini FM. Is antibiotic treatment effective in the management of chronic low back pain with disc herniation? Study protocol for a randomised controlled trial. Trials 2021; 22:759. [PMID: 34717722 PMCID: PMC8557614 DOI: 10.1186/s13063-021-05728-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 10/18/2021] [Indexed: 11/26/2022] Open
Abstract
Background There has been immense interest and debate regarding the effectiveness of antibiotic treatment for chronic low back pain. Two randomised controlled trials have examined the efficacy of antibiotics for chronic low back pain with disc herniation and Modic changes, but have reported conflicting results. The aim of this double-blind, randomised, placebo-controlled trial is to determine the efficacy of antibiotic treatment in a broader patient subgroup of chronic low back pain with disc herniation and investigate whether the presence of Modic changes predicts response to antibiotic therapy. Methods One hundred and seventy individuals with chronic low back pain will be recruited through hospital and private medical and allied health clinics; advertising in national, community and social media; and posting of flyers in community locations. They will be randomly allocated to receive either amoxicillin-clavulanate (500 mg/125 mg) twice per day for 90 days or placebo. The primary outcome measure of pain intensity will be assessed using the Low Back Pain Rating scale and a 100-mm visual analogue scale at 12 months. Secondary measures of self-reported low back disability and work absence and hindrance will also be examined, and an economic analysis will be conducted. Intention-to-treat analyses will be performed. Discussion There is uncertainty about whether antibiotic treatment is effective for chronic low back pain and, if effective, which patient subgroup is most likely to respond. We will conduct a clinical trial to investigate the efficacy of antibiotics compared with placebo in individuals with chronic low back pain and a disc herniation. Our findings will provide high-quality evidence to assist in answering these questions. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12615000958583. Registered on 11 September 2015 Supplementary Information The online version contains supplementary material available at 10.1186/s13063-021-05728-1.
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Affiliation(s)
- Donna M Urquhart
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia.
| | - Jeffrey V Rosenfeld
- Department of Neurosurgery, Alfred Hospital, Melbourne, VIC, 3004, Australia.,Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - Maurits van Tulder
- Department of Human Movement Sciences, Faculty Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081, HV, Amsterdam, The Netherlands
| | - Anita E Wluka
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Karin Leder
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Allen C Cheng
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Patrick Chan
- Department of Neurosurgery, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Richard O'Sullivan
- MRI Department, Healthcare Imaging Services, Epworth Hospital, Richmond, VIC, 3121, Australia
| | - Susan Liew
- Department of Orthopaedic Surgery, Alfred Hospital, Melbourne, VIC, 3004, Australia
| | - Flavia M Cicuttini
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, 3004, Australia
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17
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Bowden R, Forbes AB, Kasza J. Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored. Stat Methods Med Res 2021; 30:2503-2525. [PMID: 34569853 DOI: 10.1177/09622802211041754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.
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Affiliation(s)
- Rhys Bowden
- School of Public Health and Preventive Medicine, 22457Monash University, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, 22457Monash University, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, 22457Monash University, Australia
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18
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Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Med Res Methodol 2021; 21:181. [PMID: 34454418 PMCID: PMC8403376 DOI: 10.1186/s12874-021-01364-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 07/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention. METHODS We simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. RESULTS All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. CONCLUSIONS From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.
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Affiliation(s)
- Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, 533 St Kilda Road, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, 533 St Kilda Road, Melbourne, Victoria, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, 533 St Kilda Road, Melbourne, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Carling Ave, Ottawa, Ontario, 1053, Canada
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Laurier Ave E, Ottawa, Ontario, 75, Canada
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, 533 St Kilda Road, Melbourne, Victoria, Australia.
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19
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Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Med Res Methodol 2021; 21:134. [PMID: 34174809 PMCID: PMC8235830 DOI: 10.1186/s12874-021-01306-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 04/28/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. METHODS A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. RESULTS From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. CONCLUSIONS The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.
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Affiliation(s)
- Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Level 4, 553 St. Kilda Road, Melbourne, VIC, 3004, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, Level 4, 553 St. Kilda Road, Melbourne, VIC, 3004, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Level 4, 553 St. Kilda Road, Melbourne, VIC, 3004, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, ON, K1G 5Z3, Canada
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, ON, K1G 5Z3, Canada
- Department of Medicine, University of Ottawa, Roger Guindon Hall, 451 Smyth Rd, Ottawa, ON, Canada
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Level 4, 553 St. Kilda Road, Melbourne, VIC, 3004, Australia.
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20
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Korevaar E, Kasza J, Taljaard M, Hemming K, Haines T, Turner EL, Thompson JA, Hughes JP, Forbes AB. Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials. Clin Trials 2021; 18:529-540. [PMID: 34088230 DOI: 10.1177/17407745211020852] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. METHODS Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. RESULTS The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02-0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19-0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. DISCUSSION This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Terry Haines
- School of Primary and Allied Health Care, Monash University, Melbourne, VIC, Australia
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.,Duke Global Health Institute, Durham, NC, USA
| | - Jennifer A Thompson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - James P Hughes
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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21
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Corcoran TB, Myles PS, Forbes AB, Cheng AC, Bach LA, O'Loughlin E, Leslie K, Chan MTV, Story D, Short TG, Martin C, Coutts P, Ho KM. Dexamethasone and Surgical-Site Infection. N Engl J Med 2021; 384:1731-1741. [PMID: 33951362 DOI: 10.1056/nejmoa2028982] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND The glucocorticoid dexamethasone prevents nausea and vomiting after surgery, but there is concern that it may increase the risk of surgical-site infection. METHODS In this pragmatic, international, noninferiority trial, we randomly assigned 8880 adult patients who were undergoing nonurgent, noncardiac surgery of at least 2 hours' duration, with a skin incision length longer than 5 cm and a postoperative overnight hospital stay, to receive 8 mg of intravenous dexamethasone or matching placebo while under anesthesia. Randomization was stratified according to diabetes status and trial center. The primary outcome was surgical-site infection within 30 days after surgery. The prespecified noninferiority margin was 2.0 percentage points. RESULTS A total of 8725 participants were included in the modified intention-to-treat population (4372 in the dexamethasone group and 4353 in the placebo group), of whom 13.2% (576 in the dexamethasone group and 572 in the placebo group) had diabetes mellitus. Of the 8678 patients included in the primary analysis, surgical-site infection occurred in 8.1% (354 of 4350 patients) assigned to dexamethasone and in 9.1% (394 of 4328) assigned to placebo (risk difference adjusted for diabetes status, -0.9 percentage points; 95.6% confidence interval [CI], -2.1 to 0.3; P<0.001 for noninferiority). The results for superficial, deep, and organ-space surgical-site infections and in patients with diabetes were similar to those of the primary analysis. Postoperative nausea and vomiting in the first 24 hours after surgery occurred in 42.2% of patients in the dexamethasone group and in 53.9% in the placebo group (risk ratio, 0.78; 95% CI, 0.75 to 0.82). Hyperglycemic events in patients without diabetes occurred in 22 of 3787 (0.6%) in the dexamethasone group and in 6 of 3776 (0.2%) in the placebo group. CONCLUSIONS Dexamethasone was noninferior to placebo with respect to the incidence of surgical-site infection within 30 days after nonurgent, noncardiac surgery. (Funded by the Australian National Health and Medical Research Council and others; PADDI Australian New Zealand Clinical Trials Registry number, ACTRN12614001226695.).
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Affiliation(s)
- Tomás B Corcoran
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Paul S Myles
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Andrew B Forbes
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Allen C Cheng
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Leon A Bach
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Edmond O'Loughlin
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Kate Leslie
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Matthew T V Chan
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - David Story
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Timothy G Short
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Catherine Martin
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Pauline Coutts
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
| | - Kwok M Ho
- From Royal Perth Hospital (T.B.C., P.C., K.M.H.), the University of Western Australia (T.B.C., E.O., K.M.H.), Murdoch University (K.M.H.), and Fiona Stanley Hospital (E.O.), Perth, and the Alfred Hospital (P.S.M., A.C.C., L.A.B.), Monash University (T.B.C., P.S.M., A.B.F., A.C.C., L.A.B., K.L., C.M.), the University of Melbourne (K.L., D.S.), and Royal Melbourne Hospital (K.L.), Melbourne, VIC - all in Australia; the Chinese University of Hong Kong, Hong Kong (M.T.V.C.); and Auckland City Hospital and the University of Auckland - both in Auckland, New Zealand (T.G.S.)
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22
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Wang Y, Jones G, Hill C, Wluka AE, Forbes AB, Tonkin A, Hussain SM, Ding C, Cicuttini FM. Effect of atorvastatin on knee cartilage volume in patients with symptomatic knee osteoarthritis: results from a randomised placebo-controlled trial. Arthritis Rheumatol 2021; 73:2035-2043. [PMID: 33844449 DOI: 10.1002/art.41760] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/01/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To determine whether atorvastatin compared to placebo slows tibial cartilage volume loss in patients with symptomatic knee osteoarthritis in a multicentre, randomised, double-blind, placebo-controlled trial. METHODS Participants aged 40-70 years were randomised to oral atorvastatin 40 mg (n=151) or matching placebo (n=153) once daily. Primary endpoint: annual percentage change in tibial cartilage volume assessed using magnetic resonance imaging (MRI) over two years. Pre-specified secondary endpoints: progression of cartilage defects and bone marrow lesions assessed using MRI, and change in Western Ontario and McMaster Universities Osteoarthritis Index pain, stiffness and function over two years. RESULTS Of 304 participants (mean age 55.7 years, 55.6% female), 248 (81.6%) completed the trial. Annual change in tibial cartilage volume differed minimally between the atorvastatin and placebo groups (-1.66% vs. -2.17%, difference 0.50%, 95%CI -0.17% to 1.17%). There were no significant differences in progression of cartilage defects (odds ratio 0.86, 95%CI 0.52-1.41) or bone marrow lesions (odds ratio 1.00, 95%CI 0.62-1.63), change in pain [-36.0 vs. -29.5, adjusted difference -2.7, 95%CI -27.1 to 21.7), stiffness (-14.2 vs. -11.8, adjusted difference -0.2, 95%CI -12.2 to 11.8), or function [-89.4 vs. -87.5, adjusted difference 0.3, 95%CI -83.1 to 83.6). Incidence of adverse events was similar in atorvastatin (n=57, 37.7%) and placebo (n=52, 34.0%) groups. CONCLUSION Oral atorvastatin 40 mg once daily, compared with placebo, did not significantly reduce cartilage volume loss over two years in patients with symptomatic knee osteoarthritis. These findings do not support use of atorvastatin in the treatment of knee osteoarthritis.
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Affiliation(s)
- Yuanyuan Wang
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Graeme Jones
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
| | - Catherine Hill
- The Queen Elizabeth Hospital, University of Adelaide, Woodville, SA, 5011, Australia.,Department of Medicine, University of Adelaide, Adelaide, SA, 5000, Australia
| | - Anita E Wluka
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Andrew Tonkin
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Sultana Monira Hussain
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Changhai Ding
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia.,Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangdong, China
| | - Flavia M Cicuttini
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
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23
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Kasza J, Bowden R, Forbes AB. Information content of stepped wedge designs with unequal cluster-period sizes in linear mixed models: Informing incomplete designs. Stat Med 2021; 40:1736-1751. [PMID: 33438255 DOI: 10.1002/sim.8867] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 12/30/2022]
Abstract
In practice, stepped wedge trials frequently include clusters of differing sizes. However, investigations into the theoretical aspects of stepped wedge designs have, until recently, typically assumed equal numbers of subjects in each cluster and in each period. The information content of the cluster-period cells, clusters, and periods of stepped wedge designs has previously been investigated assuming equal cluster-period sizes, and has shown that incomplete stepped wedge designs may be efficient alternatives to the full stepped wedge. How this changes when cluster-period sizes are not equal is unknown, and we investigate this here. Working within the linear mixed model framework, we show that the information contributed by design components (clusters, sequences, and periods) does depend on the sizes of each cluster-period. Using a particular trial that assessed the impact of an individual education intervention on log-length of stay in rehabilitation units, we demonstrate how strongly the efficiency of incomplete designs depends on which cells are excluded: smaller incomplete designs may be more powerful than alternative incomplete designs that include a greater total number of participants. This also serves to demonstrate how the pattern of information content can be used to inform a set of incomplete designs to be considered as alternatives to the complete stepped wedge design. Our theoretical results for the information content can be extended to a broad class of longitudinal (ie, multiple period) cluster randomized trial designs.
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Affiliation(s)
- Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rhys Bowden
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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24
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Coatsworth N, Myles PS, Mann GJ, Cockburn IA, Forbes AB, Gardiner EE, Lum G, Cheng AC, Gruen RL. Prevalence of asymptomatic SARS-CoV-2 infection in elective surgical patients in Australia: a prospective surveillance study. ANZ J Surg 2021; 91:27-32. [PMID: 33421257 PMCID: PMC8013320 DOI: 10.1111/ans.16564] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/13/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND The study aimed to estimate the prevalence of active or previous SARS-CoV-2 infection in asymptomatic adults admitted for elective surgery in Australian hospitals. This surveillance activity was established as part of the National Pandemic Health Intelligence Plan. METHODS Participants (n = 3037) were recruited from 11 public and private hospitals in four states (NSW, Vic, SA and WA) between 2 June and 17 July 2020, with an overall 66% participation rate. Presence of SARS-CoV-2 viral RNA was assessed by Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR) analysis of nasopharyngeal swabs taken after induction of anaesthesia. Presence of anti-SARS-CoV-2 antibodies was assessed by analysis of serum collected at the same time using a novel dual-antigen ELISA assay. RESULTS No patient (0/3010) returned a positive RT-PCR result. The Bayesian estimated prevalence of active infection of 0.02% (95% probability interval 0.00-0.11%), with the upper endpoint being 1 in 918. Positive serology (IgG) was observed in 15 of 2991 patients, with a strong positive in five of those individuals (Bayesian estimated seroprevalence 0.16%; 95% probability interval 0.00-0.47%). CONCLUSION These results confirm that during periods of low community prevalence of SARS-CoV-2 elective surgery patients without fever or respiratory symptoms had a very low prevalence of active SARS-CoV-2 infection.
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Affiliation(s)
- Nicholas Coatsworth
- ANU Medical School, Australian National University, Canberra, Australian Capital Territory, Australia.,Infectious Diseases, The Canberra Hospital, Canberra, Australian Capital Territory, Australia.,Commonwealth Department of Health, Chief Medical Officer Group, Canberra, Australian Capital Territory, Australia
| | - Paul S Myles
- Department of Anaesthesiology and Perioperative Medicine, Alfred Hospital, Melbourne, Victoria, Australia.,Department of Anaesthesiology and Perioperative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Graham J Mann
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Ian A Cockburn
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth E Gardiner
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Gary Lum
- ANU Medical School, Australian National University, Canberra, Australian Capital Territory, Australia.,Commonwealth Department of Health, Chief Medical Officer Group, Canberra, Australian Capital Territory, Australia
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, Australia
| | - Russell L Gruen
- College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia
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25
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Leder K, Openshaw JJ, Allotey P, Ansariadi A, Barker SF, Burge K, Clasen TF, Chown SL, Duffy GA, Faber PA, Fleming G, Forbes AB, French M, Greening C, Henry R, Higginson E, Johnston DW, Lappan R, Lin A, Luby SP, McCarthy D, O'Toole JE, Ramirez-Lovering D, Reidpath DD, Simpson JA, Sinharoy SS, Sweeney R, Taruc RR, Tela A, Turagabeci AR, Wardani J, Wong T, Brown R. Study design, rationale and methods of the Revitalising Informal Settlements and their Environments (RISE) study: a cluster randomised controlled trial to evaluate environmental and human health impacts of a water-sensitive intervention in informal settlements in Indonesia and Fiji. BMJ Open 2021; 11:e042850. [PMID: 33419917 PMCID: PMC7798802 DOI: 10.1136/bmjopen-2020-042850] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
INTRODUCTION Increasing urban populations have led to the growth of informal settlements, with contaminated environments linked to poor human health through a range of interlinked pathways. Here, we describe the design and methods for the Revitalising Informal Settlements and their Environments (RISE) study, a transdisciplinary randomised trial evaluating impacts of an intervention to upgrade urban informal settlements in two Asia-Pacific countries. METHODS AND ANALYSIS RISE is a cluster randomised controlled trial among 12 settlements in Makassar, Indonesia, and 12 in Suva, Fiji. Six settlements in each country have been randomised to receive the intervention at the outset; the remainder will serve as controls and be offered intervention delivery after trial completion. The intervention involves a water-sensitive approach, delivering site-specific, modular, decentralised infrastructure primarily aimed at improving health by decreasing exposure to environmental faecal contamination. Consenting households within each informal settlement site have been enrolled, with longitudinal assessment to involve health and well-being surveys, and human and environmental sampling. Primary outcomes will be evaluated in children under 5 years of age and include prevalence and diversity of gastrointestinal pathogens, abundance and diversity of antimicrobial resistance (AMR) genes in gastrointestinal microorganisms and markers of gastrointestinal inflammation. Diverse secondary outcomes include changes in microbial contamination; abundance and diversity of pathogens and AMR genes in environmental samples; impacts on ecological biodiversity and microclimates; mosquito vector abundance; anthropometric assessments, nutrition markers and systemic inflammation in children; caregiver-reported and self-reported health symptoms and healthcare utilisation; and measures of individual and community psychological, emotional and economic well-being. The study aims to provide proof-of-concept evidence to inform policies on upgrading of informal settlements to improve environments and human health and well-being. ETHICS Study protocols have been approved by ethics boards at Monash University, Fiji National University and Hasanuddin University. TRIAL REGISTRATION NUMBER ACTRN12618000633280; Pre-results.
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Affiliation(s)
- Karin Leder
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - John J Openshaw
- Infectious Diseases and Geographic Medicine Division, Stanford University, Stanford, California, USA
| | - Pascale Allotey
- International Institute for Global Health, United Nations University, Kuala Lumpur, Malaysia
| | - Ansariadi Ansariadi
- Public Health Faculty, Hasanuddin University, Makassar, Sulawesi Selatan, Indonesia
| | - S Fiona Barker
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Kerrie Burge
- CRC for Water Sensitive Cities, Monash University, Melbourne, Victoria, Australia
| | - Thomas F Clasen
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Steven L Chown
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Grant A Duffy
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Peter A Faber
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Genie Fleming
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Matthew French
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia
| | - Chris Greening
- Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Rebekah Henry
- Civil Engineering, Monash University, Melbourne, Victoria, Australia
| | - Ellen Higginson
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - David W Johnston
- Centre for Health Economics, Monash Business School, Monash University, Melbourne, Victoria, Australia
| | - Rachael Lappan
- Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Audrie Lin
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, California, USA
| | - Stephen P Luby
- Infectious Diseases and Geographic Medicine Division, Stanford University, Stanford, California, USA
| | - David McCarthy
- Civil Engineering, Monash University, Melbourne, Victoria, Australia
| | - Joanne E O'Toole
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | | | - Daniel D Reidpath
- Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
| | - Julie A Simpson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Sheela S Sinharoy
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Rohan Sweeney
- Centre for Health Economics, Monash Business School, Monash University, Melbourne, Victoria, Australia
| | - Ruzka R Taruc
- Public Health Faculty, Hasanuddin University, Makassar, Sulawesi Selatan, Indonesia
| | - Autiko Tela
- School of Public Health and Primary Care, Fiji National University, College of Medicine, Nursing and Health Sciences, Tamavua Campus, Suva, Rewa, Fiji
| | - Amelia R Turagabeci
- School of Public Health and Primary Care, Fiji National University, College of Medicine, Nursing and Health Sciences, Tamavua Campus, Suva, Rewa, Fiji
| | - Jane Wardani
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia
| | - Tony Wong
- CRC for Water Sensitive Cities, Monash University, Melbourne, Victoria, Australia
| | - Rebekah Brown
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia
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26
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Korevaar E, Karahalios A, Forbes AB, Turner SL, McDonald S, Taljaard M, Grimshaw JM, Cheng AC, Bero L, McKenzie JE. Methods used to meta-analyse results from interrupted time series studies: A methodological systematic review protocol. F1000Res 2020; 9:110. [PMID: 33163155 PMCID: PMC7607479 DOI: 10.12688/f1000research.22226.3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/09/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Systematic reviews are used to inform healthcare decision making. In reviews that aim to examine the effects of organisational, policy change or public health interventions, or exposures, evidence from interrupted time series (ITS) studies may be included. A core component of many systematic reviews is meta-analysis, which is the statistical synthesis of results across studies. There is currently a lack of guidance informing the choice of meta-analysis methods for combining results from ITS studies, and there have been no studies examining the meta-analysis methods used in practice. This study therefore aims to describe current meta-analysis methods used in a cohort of reviews of ITS studies. Methods: We will identify the 100 most recent reviews (published between 1 January 2000 and 11 October 2019) that include meta-analyses of ITS studies from a search of eight electronic databases covering several disciplines (public health, psychology, education, economics). Study selection will be undertaken independently by two authors. Data extraction will be undertaken by one author, and for a random sample of the reviews, two authors. From eligible reviews we will extract details at the review level including discipline, type of interruption and any tools used to assess the risk of bias / methodological quality of included ITS studies; at the meta-analytic level we will extract type of outcome, effect measure(s), meta-analytic methods, and any methods used to re-analyse the individual ITS studies. Descriptive statistics will be used to summarise the data. Conclusions: This review will describe the methods used to meta-analyse results from ITS studies. Results from this review will inform future methods research examining how different meta-analysis methods perform, and ultimately, the development of guidance.
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Affiliation(s)
- Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Steve McDonald
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, K1Y 4E9, Canada.,School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, K1Y 4E9, Canada.,School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada.,Department of Medicine, University of Ottawa, Ottawa, Ontario, K1H 8M5, Canada
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.,Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, 3004, Australia
| | - Lisa Bero
- Faculty of Medicine and Health and Charles Perkins Centre, University of Sydney, Sydney, New South Wales, 2006, Australia
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
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27
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Affiliation(s)
- Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Charles Weijer
- Departments of Medicine, Epidemiology, and Biostatistics, and Philosophy, Western University, London, ON, Canada
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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28
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Hemming K, Hughes JP, McKenzie JE, Forbes AB. Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis. Stat Methods Med Res 2020; 30:376-395. [PMID: 32955403 PMCID: PMC8173367 DOI: 10.1177/0962280220948550] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.
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Affiliation(s)
- Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - James P Hughes
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Korevaar E, Cheng AC, Bero L, McKenzie JE. Creating effective interrupted time series graphs: Review and recommendations. Res Synth Methods 2020; 12:106-117. [PMID: 32657532 PMCID: PMC7818488 DOI: 10.1002/jrsm.1435] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/18/2020] [Accepted: 07/09/2020] [Indexed: 11/30/2022]
Abstract
Introduction Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short‐ and long‐term impact of an interruption. Further, well‐constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta‐analyses. Aim We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013‐2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re‐use of the data in systematic reviews and meta‐analyses. Application of data visualization recommendations can improve quality of interrupted time series graphs. Well‐designed graphs accurately depict time series data, any impact of the interruption, and the results of the analysis. Well‐designed graphs facilitate data extraction for use in systematic reviews and reproducibility. An assessment of graphs included in interrupted time series studies (published between 2013 and 2017) found that graphs often do not meet core graphing recommendations.
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Affiliation(s)
- Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.,Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, Australia
| | - Lisa Bero
- Faculty of Medicine and Health, School of Pharmacy and Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Young PJ, Bagshaw SM, Forbes AB, Nichol AD, Wright SE, Bellomo R, Haren FV, Litton E, Webb SA. Opportunities and challenges of clustering, crossing over, and using registry data in the PEPTIC trial. CRIT CARE RESUSC 2020. [DOI: 10.51893/2020.2.ed2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Proton Pump Inhibitors (PPIs) versus Histamine-2 Receptor Blockers (H2RBs) for Ulcer Prophylaxis Therapy in the Intensive Care Unit (ICU) (PEPTIC) trial is the largest randomised clinical trial ever conducted in the field of intensive care medicine. The potential clinical implications of the trial have been the subject of a previous editorial. Here we focus on the implications of the study for clinical trial science and on the opportunities the study provides for exploratory analyses that will potentially shed further light on the relative safety and efficacy of using PPIs or H2RBs for stress ulcer prophylaxis in the critically ill.
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Young PJ, Bagshaw SM, Forbes AB, Nichol AD, Wright SE, Bellomo R, van Haren F, Litton E, Webb SA. Opportunities and challenges of clustering, crossing over, and using registry data in the PEPTIC trial. CRIT CARE RESUSC 2020; 22:105-109. [PMID: 32389102 PMCID: PMC10692457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Affiliation(s)
- Paul J Young
- Medical Research Institute of New Zealand, Wellington, New Zealand.
| | - Sean M Bagshaw
- Department of Critical Care Medicine, University of Alberta Hospital, Alberta, Canada
| | - Andrew B Forbes
- Biostatistics Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | | | - Stephen E Wright
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne, United Kingdom
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, VIC, Australia
| | - Frank van Haren
- Intensive Care Unit, Canberra Hospital, Canberra, ACT, Australia
| | - Edward Litton
- Intensive Care Unit, Fiona Stanley Hospital, Perth, WA, Australia
| | - Steve A Webb
- Intensive Care Unit, Royal Perth Hospital, Perth, WA, Australia
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McGuinness SL, O'Toole J, Forbes AB, Boving TB, Patil K, D'Souza F, Gaonkar CA, Giriyan A, Barker SF, Cheng AC, Sinclair M, Leder K. A Stepped Wedge Cluster-Randomized Trial Assessing the Impact of a Riverbank Filtration Intervention to Improve Access to Safe Water on Health in Rural India. Am J Trop Med Hyg 2020; 102:497-506. [PMID: 31264565 DOI: 10.4269/ajtmh.19-0260] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Sustainable and low-cost methods for delivery of safe drinking water in resource-limited settings remain suboptimal, which contributes to global diarrhea morbidity. We aimed to assess whether delivery of riverbank filtration-treated water to newly installed water storage tanks (improved quality and access, intervention condition) reduced reported diarrhea in comparison to delivery of unfiltered river water (improved access alone, control condition) in rural Indian villages. We used a stepped wedge cluster-randomized trial (SW-CRT) design involving four clusters (villages). Selection criteria included village size, proximity to a river, and lack of existing or planned community-level safe water sources. All adults and children were eligible for enrollment. All villages started in the control condition and were sequentially randomized to receive the intervention at 3-month intervals. Our primary outcome was 7-day-period prevalence of self- or caregiver-reported diarrhea, measured at 3-month intervals (five time points). Analysis was by intention to treat. Because blinding was not possible, we incorporated questions about symptoms unrelated to water consumption to check response validity (negative control symptoms). We measured outcomes in 2,222 households (9,836 participants). We did not find a measurable reduction in diarrhea post-intervention (RR: 0.98 [95% CI: 0.24-4.09]); possible explanations include low intervention uptake, availability of other safe water sources, low baseline diarrheal prevalence, and reporting fatigue. Our study highlights both the difficulties in evaluating the impact of real-world interventions and the potential for an optimized SW-CRT design to address budgetary, funding, and logistical constraints inherent in such evaluations.
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Affiliation(s)
- Sarah L McGuinness
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Joanne O'Toole
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Thomas B Boving
- Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island.,Department of Geosciences, University of Rhode Island, Kingston, Rhode Island
| | - Kavita Patil
- The Energy and Resources Institute (TERI), Goa, India
| | | | | | - Asha Giriyan
- The Energy and Resources Institute (TERI), Goa, India
| | - S Fiona Barker
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Martha Sinclair
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Karin Leder
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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McGuinness SL, O'Toole J, Barker SF, Forbes AB, Boving TB, Giriyan A, Patil K, D'Souza F, Vhaval R, Cheng AC, Leder K. Household Water Storage Management, Hygiene Practices, and Associated Drinking Water Quality in Rural India. Environ Sci Technol 2020; 54:4963-4973. [PMID: 32167297 DOI: 10.1021/acs.est.9b04818] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Household drinking water storage is commonly practiced in rural India. Fecal contamination may be introduced at the water source, during collection, storage, or access. Within a trial of a community-level water supply intervention, we conducted five quarterly household-level surveys to collect information about water, sanitation, and hygiene practices in rural India. In a random subsample of households, we tested stored drinking water samples for Escherichia coli, concurrently observing storage and access practices. We conducted 9961 surveys and collected 3296 stored water samples. Stored water samples were frequently contaminated with E. coli (69%), and E. coli levels were the highest during the wet season. Most households contributing two or more drinking water samples had detectable E. coli in some (47%) or all (44%) samples. Predictors of stored water contamination with E. coli included consumption of river water and open defecation; consumption of reverse osmosis-treated water and safe water access practices appeared to be protective. Until households can be reached with on-premises continuous safe water supplies, suboptimal household water storage practices are likely to continue. Improvements to source water quality alone are unlikely to prevent exposure to contaminated drinking water unless attention is also given to improving household water storage, access, and sanitation practices.
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Affiliation(s)
- Sarah L McGuinness
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Joanne O'Toole
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - S Fiona Barker
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Thomas B Boving
- Department of Geosciences & Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island 02281, United States
| | - Asha Giriyan
- The Energy and Resources Institute (TERI), Southern Regional Centre, Santa Cruz, Goa 403005, India
| | - Kavita Patil
- The Energy and Resources Institute (TERI), Southern Regional Centre, Santa Cruz, Goa 403005, India
| | - Fraddry D'Souza
- The Energy and Resources Institute (TERI), Southern Regional Centre, Santa Cruz, Goa 403005, India
| | - Ramkrishna Vhaval
- The Energy and Resources Institute (TERI), Southern Regional Centre, Santa Cruz, Goa 403005, India
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Karin Leder
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
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Kasza J, Hooper R, Copas A, Forbes AB. Sample size and power calculations for open cohort longitudinal cluster randomized trials. Stat Med 2020; 39:1871-1883. [PMID: 32133688 PMCID: PMC7217159 DOI: 10.1002/sim.8519] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/15/2020] [Accepted: 02/17/2020] [Indexed: 01/24/2023]
Abstract
When calculating sample size or power for stepped wedge or other types of longitudinal cluster randomized trials, it is critical that the planned sampling structure be accurately specified. One common assumption is that participants will provide measurements in each trial period, that is, a closed cohort, and another is that each participant provides only one measurement during the course of the trial. However some studies have an "open cohort" sampling structure, where participants may provide measurements in variable numbers of periods. To date, sample size calculations for longitudinal cluster randomized trials have not accommodated open cohorts. Feldman and McKinlay (1994) provided some guidance, stating that the participant-level autocorrelation could be varied to account for the degree of overlap in different periods of the study, but did not indicate precisely how to do so. We present sample size and power formulas that allow for open cohorts and discuss the impact of the degree of "openness" on sample size and power. We consider designs where the number of participants in each cluster will be maintained throughout the trial, but individual participants may provide differing numbers of measurements. Our results are a unification of closed cohort and repeated cross-sectional sample results of Hooper et al (2016), and indicate precisely how participant autocorrelation of Feldman and McKinlay should be varied to account for an open cohort sampling structure. We discuss different types of open cohort sampling schemes and how open cohort sampling structure impacts on power in the presence of decaying within-cluster correlations and autoregressive participant-level errors.
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Affiliation(s)
- Jessica Kasza
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Richard Hooper
- Centre for Primary Care and Public HealthQueen Mary University of LondonLondonUK
| | - Andrew Copas
- MRC Clinical Trials UnitUniversity College LondonLondonUK
| | - Andrew B. Forbes
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
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35
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Young PJ, Bagshaw SM, Forbes AB, Nichol AD, Wright SE, Bailey M, Bellomo R, Beasley R, Brickell K, Eastwood GM, Gattas DJ, van Haren F, Litton E, Mackle DM, McArthur CJ, McGuinness SP, Mouncey PR, Navarra L, Opgenorth D, Pilcher D, Saxena MK, Webb SA, Wiley D, Rowan KM. Effect of Stress Ulcer Prophylaxis With Proton Pump Inhibitors vs Histamine-2 Receptor Blockers on In-Hospital Mortality Among ICU Patients Receiving Invasive Mechanical Ventilation: The PEPTIC Randomized Clinical Trial. JAMA 2020; 323:616-626. [PMID: 31950977 PMCID: PMC7029750 DOI: 10.1001/jama.2019.22190] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
IMPORTANCE Proton pump inhibitors (PPIs) or histamine-2 receptor blockers (H2RBs) are often prescribed for patients as stress ulcer prophylaxis drugs in the intensive care unit (ICU). The comparative effect of these drugs on mortality is unknown. OBJECTIVE To compare in-hospital mortality rates using PPIs vs H2RBs for stress ulcer prophylaxis. DESIGN, SETTING, AND PARTICIPANTS Cluster crossover randomized clinical trial conducted at 50 ICUs in 5 countries between August 2016 and January 2019. Patients requiring invasive mechanical ventilation within 24 hours of ICU admission were followed up for 90 days at the hospital. INTERVENTIONS Two stress ulcer prophylaxis strategies were compared (preferential use with PPIs vs preferential use with H2RBs). Each ICU used each strategy sequentially for 6 months in random order; 25 ICUs were randomized to the sequence with use of PPIs and then use of H2RBs and 25 ICUs were randomized to the sequence with use of H2RBs and then use of PPIs (13 436 patients randomized by site to PPIs and 13 392 randomized by site to H2RBs). MAIN OUTCOMES AND MEASURES The primary outcome was all-cause mortality within 90 days during index hospitalization. Secondary outcomes were clinically important upper gastrointestinal bleeding, Clostridioides difficile infection, and ICU and hospital lengths of stay. RESULTS Among 26 982 patients who were randomized, 154 opted out, and 26 828 were analyzed (mean [SD] age, 58 [17.0] years; 9691 [36.1%] were women). There were 26 771 patients (99.2%) included in the mortality analysis; 2459 of 13 415 patients (18.3%) in the PPI group died at the hospital by day 90 and 2333 of 13 356 patients (17.5%) in the H2RB group died at the hospital by day 90 (risk ratio, 1.05 [95% CI, 1.00 to 1.10]; absolute risk difference, 0.93 percentage points [95% CI, -0.01 to 1.88] percentage points; P = .054). An estimated 4.1% of patients randomized by ICU site to PPIs actually received H2RBs and an estimated 20.1% of patients randomized by ICU site to H2RBs actually received PPIs. Clinically important upper gastrointestinal bleeding occurred in 1.3% of the PPI group and 1.8% of the H2RB group (risk ratio, 0.73 [95% CI, 0.57 to 0.92]; absolute risk difference, -0.51 percentage points [95% CI, -0.90 to -0.12 percentage points]; P = .009). Rates of Clostridioides difficile infection and ICU and hospital lengths of stay were not significantly different by treatment group. One adverse event (an allergic reaction) was reported in 1 patient in the PPI group. CONCLUSIONS AND RELEVANCE Among ICU patients requiring mechanical ventilation, a strategy of stress ulcer prophylaxis with use of proton pump inhibitors vs histamine-2 receptor blockers resulted in hospital mortality rates of 18.3% vs 17.5%, respectively, a difference that did not reach the significance threshold. However, study interpretation may be limited by crossover in the use of the assigned medication. TRIAL REGISTRATION anzctr.org.au Identifier: ACTRN12616000481471.
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Affiliation(s)
| | - Paul J Young
- Medical Research Institute of New Zealand, Wellington
- Intensive Care Unit, Wellington Hospital, Wellington, New Zealand
| | - Sean M Bagshaw
- Department of Critical Care Medicine, University of Alberta Hospital, Edmonton, Canada
| | | | - Alistair D Nichol
- Intensive Care Unit, Alfred Hospital, Melbourne, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
- University College Dublin-Clinical Research Centre, St Vincent's Hospital, Dublin, Ireland
| | - Stephen E Wright
- Intensive Care Unit, Freeman Hospital, Newcastle upon Tyne, England
| | - Michael Bailey
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
- Intensive Care Unit, Austin Hospital, Heidelberg, Australia
| | | | - Kathy Brickell
- University College Dublin-Clinical Research Centre, St Vincent's Hospital, Dublin, Ireland
| | | | - David J Gattas
- Intensive Care Unit, Royal Prince Alfred Hospital, Camperdown, Australia
- George Institute for Global Health, University of New South Wales, Sydney, Australia
| | | | - Edward Litton
- Intensive Care Unit, Fiona Stanley Hospital, Murdoch, Australia
| | | | - Colin J McArthur
- Medical Research Institute of New Zealand, Wellington
- Department of Critical Care Medicine, Auckland City Hospital, Auckland, New Zealand
| | - Shay P McGuinness
- Medical Research Institute of New Zealand, Wellington
- Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand
| | - Paul R Mouncey
- Intensive Care National Audit and Research Centre, London, England
| | | | - Dawn Opgenorth
- Department of Critical Care Medicine, University of Alberta Hospital, Edmonton, Canada
| | - David Pilcher
- Intensive Care Unit, Alfred Hospital, Melbourne, Australia
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
- Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Camberwell, Australia
| | - Manoj K Saxena
- George Institute for Global Health, University of New South Wales, Sydney, Australia
- Intensive Care Unit, Bankstown Hospital, Bankstown, Australia
| | - Steve A Webb
- Intensive Care Unit, Royal Perth Hospital, Perth, Australia
| | - Daisy Wiley
- Intensive Care National Audit and Research Centre, London, England
| | - Kathryn M Rowan
- Intensive Care National Audit and Research Centre, London, England
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Brennan SE, McDonald S, Page MJ, Reid J, Ward S, Forbes AB, McKenzie JE. Long-term effects of alcohol consumption on cognitive function: a systematic review and dose-response analysis of evidence published between 2007 and 2018. Syst Rev 2020; 9:33. [PMID: 32054517 PMCID: PMC7020517 DOI: 10.1186/s13643-019-1220-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Understanding the long-term health effects of low to moderate alcohol consumption is important for establishing thresholds for minimising the lifetime risk of harm. Recent research has elucidated the dose-response relationship between alcohol and cardiovascular outcomes, showing an increased risk of harm at levels of intake previously thought to be protective. The primary objective of this review was to examine (1) whether there is a dose-response relationship between levels of alcohol consumption and long-term cognitive effects, and (2) what the effects are of different levels of consumption. METHODS The review was conducted according to a pre-specified protocol. Eligible studies were those published 2007 onwards that compared cognitive function among people with different levels of alcohol consumption (measured ≥ 6 months prior to first follow-up of cognition). Major cognitive impairment was excluded. Searches were limited to MEDLINE, Embase and PsycINFO (January 2007 to April 2018). Screening, data extraction, and risk of bias assessment (ROBINS-I) were piloted by three authors, then completed by a single author and checked by a second. Analyses were undertaken to identify and characterise dose-response relationships between levels of alcohol consumption and cognition. Certainty of evidence was assessed using GRADE. RESULTS We included 27 cohort studies (from 4786 citations). Eighteen studies examined the effects of alcohol consumption at different levels (risk of bias 16 serious, 2 critical). Ten studies provided data for dose-response analysis. The pooled dose-response relationship showed a maximum standardised mean difference (SMD) indicating slightly better cognition among women with moderate alcohol consumption compared to current non-drinkers (SMD 0.18, 95%CI 0.02 to 0.34, at 14.4 grams/day; 5 studies, very low certainty evidence), and a trivial difference for men (SMD 0.05, 95% CI 0.00 to 0.10, at 19.4 grams/day; 6 studies, very low certainty evidence). CONCLUSIONS Major limitations in the design and reporting of included studies made it impossible to discern if the effects of 'lower' levels of alcohol intake are due to bias. Further review of the evidence is unlikely to resolve this issue without meta-analysis of individual patient data from cohort studies that address biases in the selection of participants and classification of alcohol consumption.
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Affiliation(s)
- Sue E. Brennan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Steve McDonald
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Matthew J. Page
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jane Reid
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephanie Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Joanne E. McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Williamson EJ, Polak J, Simpson JA, Giles GG, English DR, Hodge A, Gurrin L, Forbes AB. Sustained adherence to a Mediterranean diet and physical activity on all-cause mortality in the Melbourne Collaborative Cohort Study: application of the g-formula. BMC Public Health 2019; 19:1733. [PMID: 31878916 PMCID: PMC6933918 DOI: 10.1186/s12889-019-7919-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 11/08/2019] [Indexed: 12/27/2022] Open
Abstract
Background Adherence to a traditional Mediterranean diet has been associated with lower mortality and cardiovascular disease risk. The relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time remains unknown. Methods We used the parametric G-formula to account for time-dependent confounding, in order to assess the relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time. We included healthy Melbourne Collaborative Cohort Study participants attending a visit during 1995–1999. Questionnaires assessed diet and physical activity at each of three study waves. Deaths were identified by linkage to national registries. We estimated mortality risk over approximately 14 years (1995–2011). Results Of 22,213 participants, 2163 (9.7%) died during 13.6 years median follow-up. Sustained high physical activity and adherence to a Mediterranean-style diet resulted in an estimated reduction in all-cause mortality of 1.82 per 100 people (95% confidence interval (CI): 0.03, 3.6). The population attributable fraction was 13% (95% CI: 4, 23%) for sustained high physical activity, 7% (95% CI: − 3, 17%) for sustained adherence to a Mediterranean-style diet and 18% (95% CI: 0, 36%) for their combination. Conclusions A small reduction in mortality may be achieved by sustained elevated physical activity levels in healthy middle-aged adults, but there may be comparatively little gain from increasing adherence to a Mediterranean-style diet.
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Affiliation(s)
- Elizabeth J Williamson
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK. .,, Health Data Research UK (HDR UK), UK.
| | - Julia Polak
- The Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia
| | - Julie A Simpson
- The Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Allison Hodge
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Lyle Gurrin
- The Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- The Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.,Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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38
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Mittinty MN, Lynch JW, Forbes AB, Gurrin LC. Effect decomposition through multiple causally nonordered mediators in the presence of exposure-induced mediator-outcome confounding. Stat Med 2019; 38:5085-5102. [PMID: 31475385 DOI: 10.1002/sim.8352] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 05/27/2019] [Accepted: 07/28/2019] [Indexed: 11/08/2022]
Abstract
Avin et al (2005) showed that, in the presence of exposure-induced mediator-outcome confounding, decomposing the total causal effect (TCE) using standard conditional exchangeability assumptions is not possible even under a nonparametric structural equation model with all confounders observed. Subsequent research has investigated the assumptions required for such a decomposition to be identifiable and estimable from observed data. One approach was proposed by VanderWeele et al (2014). They decomposed the TCE under three different scenarios: (1) treating the mediator and the exposure-induced confounder as joint mediators; (2) generating path-specific effects albeit without distinguishing between multiple distinct paths through the exposure-induced confounder; and (3) using so-called randomised interventional analogues where sampling values from the distribution of the mediator within the levels of the exposure effectively marginalises over the exposure-induced confounder. In this paper, we extend their approach to the case where there are multiple mediators that do not influence each other directly but which are all influenced by an exposure-induced mediator-outcome confounder. We provide a motivating example and results from a simulation study based on from our work in dental epidemiology featuring the 1982 Pelotas Birth Cohort in Brazil.
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Affiliation(s)
- Murthy N Mittinty
- School of Public Health, University of Adelaide, Adelaide, South Australia, Australia
| | - John W Lynch
- School of Public Health, University of Adelaide, Adelaide, South Australia, Australia.,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Andrew B Forbes
- School of Population Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Lyle C Gurrin
- School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
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Corcoran TB, Myles PS, Forbes AB, O'Loughlin E, Leslie K, Story D, Short TG, Chan MT, Coutts P, Sidhu J, Cheng AC, Bach LA, Ho KM. The perioperative administration of dexamethasone and infection (PADDI) trial protocol: rationale and design of a pragmatic multicentre non-inferiority study. BMJ Open 2019; 9:e030402. [PMID: 31494615 PMCID: PMC6731833 DOI: 10.1136/bmjopen-2019-030402] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION The intraoperative administration of dexamethasone for prophylaxis against postoperative nausea and vomiting is a common and recommended practice. The safety of the administration of this immunosuppressive agent at a time of significant immunological disruption has not been rigorously evaluated in terms of infective complications. METHODS/ANALYSIS This is a pragmatic, multicentre, randomised, controlled, non-inferiority trial. A total of 8880 patients undergoing elective major surgery will be enrolled. Participants will be randomly allocated to receive either dexamethasone 8 mg or placebo intravenously following the induction of anaesthesia in a 1:1 ratio, stratified by centre and diabetes status. Patient enrolment into the trial is ongoing. The primary outcome is surgical site infection at 30 days following surgery, defined according to the Centre for Disease Control criteria. ETHICS/DISSEMINATION The PADDI trial has been approved by the ethics committees of over 45 participating sites in Australia, New Zealand, Hong Kong, South Africa and the Netherlands. The trial has been endorsed by the Australia and New Zealand College of Anaesthetists Clinical Trials Network and the Australian Society for Infectious Diseases Clinical Research Network. Participant recruitment began in March 2016 and is expected to be complete in mid-2019. Publication of the results of the PADDI trial is anticipated to occur in early 2020. TRIAL REGISTRATION NUMBER ACTRN12614001226695.
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Affiliation(s)
- Tomás B Corcoran
- Royal Perth Hospital, Perth, Western Australia, Australia
- University of Western Australia, Perth, Western Australia, Australia
- Monash University, Melbourne, Victoria, Australia
| | - Paul S Myles
- Monash University, Melbourne, Victoria, Australia
- Alfred Hospital, Melbourne, Victoria, Australia
| | | | - Ed O'Loughlin
- University of Western Australia, Perth, Western Australia, Australia
- Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Kate Leslie
- Monash University, Melbourne, Victoria, Australia
- Royal Melbourne Hospital, Melbourne, Victoria, Australia
- The University of Melbourne, Melbourne, Victoria, Australia
| | - David Story
- Royal Melbourne Hospital, Melbourne, Victoria, Australia
- The University of Melbourne, Melbourne, Victoria, Australia
| | | | | | - Pauline Coutts
- Royal Perth Hospital, Perth, Western Australia, Australia
| | | | - Allen C Cheng
- Monash University, Melbourne, Victoria, Australia
- Alfred Hospital, Melbourne, Victoria, Australia
| | - Leon A Bach
- Monash University, Melbourne, Victoria, Australia
- Alfred Hospital, Melbourne, Victoria, Australia
| | - Kwok M Ho
- Royal Perth Hospital, Perth, Western Australia, Australia
- University of Western Australia, Perth, Western Australia, Australia
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40
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Grantham KL, Kasza J, Heritier S, Hemming K, Litton E, Forbes AB. How many times should a cluster randomized crossover trial cross over? Stat Med 2019; 38:5021-5033. [PMID: 31475383 DOI: 10.1002/sim.8349] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 01/18/2023]
Abstract
Trial planning requires making efficient yet practical design choices. In a cluster randomized crossover trial, clusters of subjects cross back and forth between implementing the control and intervention conditions over the course of the trial, with each crossover marking the start of a new period. If it is possible to set up such a trial with more crossovers, a pertinent question is whether there are efficiency gains from clusters crossing over more frequently, and if these gains are substantial enough to justify the added complexity and cost of implementing more crossovers. We seek to determine the optimal number of crossovers for a fixed trial duration, and then identify other highly efficient designs by allowing the total number of clusters to vary and imposing thresholds on maximum cost and minimum statistical power. Our results pertain to trials with continuous recruitment and a continuous primary outcome, with the treatment effect estimated using a linear mixed model. To account for the similarity between subjects' outcomes within a cluster, we assume a correlation structure in which the correlation decays gradually in a continuous manner as the time between subjects' measurements increases. The optimal design is characterized by crossovers between the control and intervention conditions with each successive subject. However, this design is neither practical nor cost-efficient to implement, nor is it necessary: the gains in efficiency increase sharply in moving from a two-period to a four-period trial design, but approach an asymptote for the scenarios considered as the number of crossovers continues to increase.
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Affiliation(s)
- Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Edward Litton
- Intensive Care Unit, Fiona Stanley Hospital, Murdoch, Australia
- School of Medicine, University of Western Australia, Perth, Australia
- Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Camberwell, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Kasza J, Taljaard M, Forbes AB. Information content of stepped-wedge designs when treatment effect heterogeneity and/or implementation periods are present. Stat Med 2019; 38:4686-4701. [PMID: 31321806 DOI: 10.1002/sim.8327] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 06/26/2019] [Accepted: 06/28/2019] [Indexed: 01/04/2023]
Abstract
Stepped-wedge cluster randomized trials, which randomize clusters of subjects to treatment sequences in which clusters switch from control to intervention conditions, are being conducted with increasing frequency. Due to the real-world nature of this design, methodological and implementation challenges are ubiquitous. To account for such challenges, more complex statistical models to plan studies and analyze data are required. In this paper, we consider stepped-wedge trials that accommodate treatment effect heterogeneity across clusters, implementation periods during which no data are collected, or both treatment effect heterogeneity and implementation periods. Previous work has shown that the sequence-period cells of a stepped-wedge design contribute unequal amounts of information to the estimation of the treatment effect. In this paper, we extend that work by considering the amount of information available for the estimation of the treatment effect in each sequence-period cell, sequence, and period of stepped-wedge trials with more complex designs and outcome models. When either treatment effect heterogeneity and/or implementation periods are present, the pattern of information content of sequence-period cells tends to be clustered around the times of the switch from control to intervention condition, similarly to when these complexities are absent. However, the presence and degree of treatment effect heterogeneity and the number of implementation periods can influence the information content of periods and sequences markedly.
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Affiliation(s)
- Jessica Kasza
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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42
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Affiliation(s)
- Kelsey L. Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew B. Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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43
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Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, Bero L, McKenzie JE. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review. BMJ Open 2019; 9:e024096. [PMID: 30696676 PMCID: PMC6352832 DOI: 10.1136/bmjopen-2018-024096] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation. METHODS AND ANALYSIS We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data. ETHICS AND DISSEMINATION Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.
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Affiliation(s)
- Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, Australia
| | - Lisa Bero
- Faculty of Pharmacy and Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Grantham KL, Kasza J, Heritier S, Hemming K, Forbes AB. Accounting for a decaying correlation structure in cluster randomized trials with continuous recruitment. Stat Med 2019; 38:1918-1934. [PMID: 30663132 DOI: 10.1002/sim.8089] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 11/11/2018] [Accepted: 12/13/2018] [Indexed: 11/05/2022]
Abstract
A requirement for calculating sample sizes for cluster randomized trials (CRTs) conducted over multiple periods of time is the specification of a form for the correlation between outcomes of subjects within the same cluster, encoded via the within-cluster correlation structure. Previously proposed within-cluster correlation structures have made strong assumptions; for example, the usual assumption is that correlations between the outcomes of all pairs of subjects are identical ("uniform correlation"). More recently, structures that allow for a decay in correlation between pairs of outcomes measured in different periods have been suggested. However, these structures are overly simple in settings with continuous recruitment and measurement. We propose a more realistic "continuous-time correlation decay" structure whereby correlations between subjects' outcomes decay as the time between these subjects' measurement times increases. We investigate the use of this structure on trial planning in the context of a primary care diabetes trial, where there is evidence of decaying correlation between pairs of patients' outcomes over time. In particular, for a range of different trial designs, we derive the variance of the treatment effect estimator under continuous-time correlation decay and compare this to the variance obtained under uniform correlation. For stepped wedge and cluster randomized crossover designs, incorrectly assuming uniform correlation will underestimate the required sample size under most trial configurations likely to occur in practice. Planning of CRTs requires consideration of the most appropriate within-cluster correlation structure to obtain a suitable sample size.
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Affiliation(s)
- Kelsey L Grantham
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jessica Kasza
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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45
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Hunter DJ, Hinman RS, Bowden JL, Egerton T, Briggs AM, Bunker SJ, Kasza J, Forbes AB, French SD, Pirotta M, Schofield DJ, Zwar NA, Bennell KL. Correction to: Effectiveness of a new model of primary care management on knee pain and function in patients with knee osteoarthritis: Protocol for THE PARTNER STUDY. BMC Musculoskelet Disord 2018; 19:443. [PMID: 30572871 PMCID: PMC6302386 DOI: 10.1186/s12891-018-2362-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
After the publication of this protocol [1], our collaborator Prima Health solutions advised us of their intent to withdraw from the study.
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Affiliation(s)
- D J Hunter
- Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, Australia. .,Department of Rheumatology, Royal North Shore Hospital, Sydney, NSW, Australia.
| | - R S Hinman
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
| | - J L Bowden
- Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, Australia
| | - T Egerton
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
| | - A M Briggs
- School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia
| | - S J Bunker
- Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
| | - J Kasza
- Biostatistics Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - A B Forbes
- Biostatistics Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - S D French
- School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada.,Department of Chiropractic, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia
| | - M Pirotta
- Department of General Practice, The University of Melbourne, Melbourne, Victoria, Australia
| | - D J Schofield
- Department of Economics, Faculty of Business and Economics, Macquarie University, Sydney, Australia
| | - N A Zwar
- School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia.,School of Medicine, University of Wollongong, Wollongong, NSW, Australia
| | - K L Bennell
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
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Hemming K, Taljaard M, McKenzie JE, Hooper R, Copas A, Thompson JA, Dixon-Woods M, Aldcroft A, Doussau A, Grayling M, Kristunas C, Goldstein CE, Campbell MK, Girling A, Eldridge S, Campbell MJ, Lilford RJ, Weijer C, Forbes AB, Grimshaw JM. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ 2018; 363:k1614. [PMID: 30413417 PMCID: PMC6225589 DOI: 10.1136/bmj.k1614] [Citation(s) in RCA: 194] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/20/2018] [Indexed: 12/14/2022]
Affiliation(s)
- Karla Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Richard Hooper
- Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
| | - Andrew Copas
- London Hub for Trials Methodology Research, MRC Clinical Trials Unit at University College London, London, UK
| | - Jennifer A Thompson
- London Hub for Trials Methodology Research, MRC Clinical Trials Unit at University College London, London, UK
- Department for Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Mary Dixon-Woods
- The Healthcare Improvement Studies Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | | | - Adelaide Doussau
- Biomedical Ethics Unit, McGill University School of Medicine, Montreal, QC, Canada
| | | | | | - Cory E Goldstein
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | | | - Alan Girling
- Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK
| | - Sandra Eldridge
- Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
| | | | | | - Charles Weijer
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
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Li F, Forbes AB, Turner EL, Preisser JS. Power and sample size requirements for GEE analyses of cluster randomized crossover trials. Stat Med 2018; 38:636-649. [PMID: 30298551 DOI: 10.1002/sim.7995] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 07/24/2018] [Accepted: 09/15/2018] [Indexed: 12/25/2022]
Abstract
The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel cluster randomized design, which often involves a limited number of clusters. In recent years, the cluster randomized crossover design has been increasingly used to evaluate the effectiveness of health care policy or programs, and the interest often lies in quantifying the population-averaged intervention effect. In this paper, we consider the two-treatment two-period crossover design, and develop sample size procedures for continuous and binary outcomes corresponding to a population-averaged model estimated by generalized estimating equations, accounting for both within-period and interperiod correlations. In particular, we show that the required sample size depends on the correlation parameters through an eigenvalue of the within-cluster correlation matrix for continuous outcomes and through two distinct eigenvalues of the correlation matrix for binary outcomes. We demonstrate that the empirical power corresponds well with the predicted power by the proposed formulae for as few as eight clusters, when outcomes are analyzed using the matrix-adjusted estimating equations for the correlation parameters concurrently with a suitable bias-corrected sandwich variance estimator.
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Affiliation(s)
- Fan Li
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.,Duke Clinical Research Institute, Durham, North Carolina
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth L Turner
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.,Duke Global Health Institute, Durham, North Carolina
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Kasza J, Forbes AB. Inference for the treatment effect in multiple-period cluster randomised trials when random effect correlation structure is misspecified. Stat Methods Med Res 2018; 28:3112-3122. [PMID: 30189794 DOI: 10.1177/0962280218797151] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Multiple-period cluster randomised trials, such as stepped wedge or cluster cross-over trials, are being conducted with increasing frequency. In the design and analysis of these trials, it is necessary to specify the form of the within-cluster correlation structure, and a common assumption is that the correlation between the outcomes of any pair of subjects within a cluster is identical. More complex models that allow for correlations within a cluster to decay over time have recently been suggested. However, most software packages cannot fit these models. As a result, practitioners may choose a simpler model. We analytically examine the impact of incorrectly omitting a decay in correlation on the variance of the treatment effect estimator and show that misspecification of the within-cluster correlation structure can lead to incorrect conclusions regarding estimated treatment effects for stepped wedge and cluster crossover trials.
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Affiliation(s)
- Jessica Kasza
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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49
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Kasza J, Forbes AB. Information content of cluster-period cells in stepped wedge trials. Biometrics 2018; 75:144-152. [PMID: 30051909 DOI: 10.1111/biom.12959] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 06/01/2018] [Accepted: 07/01/2018] [Indexed: 11/26/2022]
Abstract
Stepped wedge and other multiple-period cluster randomized trials, which collect data from multiple clusters across multiple time periods, are being conducted with increasing frequency; statistical research into these designs has not kept apace. In particular, some stepped wedge designs with missing cluster-period "cells" have been proposed without any formal justification. Indeed there are no general guidelines regarding which cells of a stepped wedge design contribute the least information toward estimation of the treatment effect, and correspondingly which may be preferentially omitted. In this article, we define a metric of the information content of cluster-period cells, entire treatment sequences, and entire periods of the standard stepped wedge design as the increase in variance of the estimator of the treatment effect when that cell, sequence, or period is omitted. We show that the most information-rich cells are those that occur immediately before or after treatment switches, but also that there are additional cells that contribute almost as much to the estimation of the treatment effect. However, the information content patterns depend on the assumed correlation structure for the repeated measurements within a cluster.
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Affiliation(s)
- Jessica Kasza
- Department of Epidemiology and Preventive Medicine, Monash University, 553 St. Kilda Road, Melbourne 3004, Australia
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, Monash University, 553 St. Kilda Road, Melbourne 3004, Australia
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50
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McGuinness SL, Barker SF, O'Toole J, Cheng AC, Forbes AB, Sinclair M, Leder K. Effect of hygiene interventions on acute respiratory infections in childcare, school and domestic settings in low- and middle-income countries: a systematic review. Trop Med Int Health 2018; 23:816-833. [PMID: 29799658 DOI: 10.1111/tmi.13080] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
OBJECTIVES Acute respiratory infections (ARIs) disproportionately affect those living in low- and middle-income countries (LMICs). We aimed to determine whether hygiene interventions delivered in childcare, school or domestic settings in LMICs effectively prevent or reduce ARIs. METHODS We registered our systematic review with PROSPERO (CRD42017058239) and searched MEDLINE, EMBASE, CENTRAL, and Scopus from inception to 17 October 2017 for randomised controlled trials (RCTs) examining the impact of hygiene interventions on ARI morbidity in adults and children in community-based settings in LMICs. We stratified data into childcare, school and domestic settings and used the Grading of Recommendations Assessment, Development and Evaluation approach to assess evidence quality. RESULTS We identified 14 cluster RCTs evaluating hand-hygiene interventions in LMICs with considerable heterogeneity in setting, size, intervention delivery and duration. We found reduced ARI-related absenteeism and illness in childcare settings (low- to moderate-quality evidence). In school settings, we found reduced ARI-related absenteeism and laboratory-confirmed influenza (moderate- to high-quality evidence), but no reduction in ARI illness (low-quality evidence). In domestic settings, we found reduced ARI illness and pneumonia amongst children in urban settlements (high-quality evidence) but not in rural settlements (low-quality evidence), and no effect on secondary transmission of influenza in households (moderate-quality evidence). CONCLUSIONS Evidence suggests that hand-hygiene interventions delivered in childcare, school and domestic settings can reduce ARI morbidity, but effectiveness varies according to setting, intervention target and intervention compliance. Further studies are needed to develop, deliver and evaluate targeted and sustainable hygiene interventions in LMICs.
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Affiliation(s)
- Sarah L McGuinness
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Department of Infectious Diseases, Alfred Hospital, Melbourne, VIC, Australia
| | - S Fiona Barker
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Joanne O'Toole
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Allen C Cheng
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Department of Infectious Diseases, Alfred Hospital, Melbourne, VIC, Australia
| | - Andrew B Forbes
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Martha Sinclair
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Karin Leder
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, VIC, Australia
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