1
|
Qi H, Rizopoulos D, van Rosmalen J. Incorporating historical control information in ANCOVA models using the meta-analytic-predictive approach. Res Synth Methods 2022; 13:681-696. [PMID: 35439840 PMCID: PMC9790567 DOI: 10.1002/jrsm.1561] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 02/09/2022] [Accepted: 04/07/2022] [Indexed: 12/31/2022]
Abstract
The meta-analytic-predictive (MAP) approach is a Bayesian meta-analytic method to synthesize and incorporate information from historical controls in the analysis of a new trial. Classically, only a single parameter, typically the intercept or rate, is assumed to vary across studies, which may not be realistic in more complex models. Analysis of covariance (ANCOVA) is often used to analyze trials with a pretest-posttest design, where both the intercept and the baseline effect (coefficient of the outcome at baseline) affect the estimated treatment effect. We extended the MAP approach to ANCOVA, to allow for variation in the intercept and the baseline effect across studies, and possibly also correlation between these parameters. The method was illustrated using data from the Alzheimer's Disease Cooperative Study (ADCS) and assessed with a simulation study. In the ADCS data, the proposed multivariate MAP approach yielded a prior effective sample size of 79 and 58 for the intercept and the baseline effect respectively and reduced the posterior standard deviation of the treatment effect by 12.6%. The result was robust to the choice of prior for the between-study variation. In the simulations, the proposed approach yielded power gains with a good control of the type I error rate. Ignoring the between-study correlation of the parameters or assuming no variation in the baseline effect generally led to less power gain. In conclusion, the MAP approach can be extended to a multivariate version for ANCOVA, which may improve the estimation of the treatment effect.
Collapse
Affiliation(s)
- Hongchao Qi
- Department of BiostatisticsErasmus University Medical CenterRotterdamthe Netherlands,Department of EpidemiologyErasmus University Medical CenterRotterdamthe Netherlands
| | - Dimitris Rizopoulos
- Department of BiostatisticsErasmus University Medical CenterRotterdamthe Netherlands,Department of EpidemiologyErasmus University Medical CenterRotterdamthe Netherlands
| | - Joost van Rosmalen
- Department of BiostatisticsErasmus University Medical CenterRotterdamthe Netherlands,Department of EpidemiologyErasmus University Medical CenterRotterdamthe Netherlands
| |
Collapse
|
2
|
An investigation of the constancy of effect in Cochrane systematic reviews in context with the assumptions for noninferiority trials. BMC Med Res Methodol 2022; 22:204. [PMID: 35879673 PMCID: PMC9316704 DOI: 10.1186/s12874-022-01684-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
When designing a noninferiority (NI) study one of the most important steps is to set the noninferiority (NI) limit. The NI limit is an acceptable loss of efficacy for a new investigative treatment compared to an active control treatment - often standard care. The limit should be a value so small that the loss efficacy is clinically zero. An approach to the setting of a noninferiority limit such that an effect over placebo can be shown through an indirect comparison to placebo-controlled trials where the active control treatment was compared to placebo. In this context, the setting of the NI limit depends on three assumptions: assay sensitivity, bias minimisation, and the constancy assumption. The last assumption of constancy assumes the effect of the active control over placebo is constant. This paper aims to assess the constancy assumption in placebo-controlled trials. METHODS 236 Cochrane reviews of placebo-controlled trials published in 2015-2016 were collected and used to assess the relation between the placebo, active treatment, and the standardised treatment different (SMD) with the time (year of publication). RESULTS The analysis showed that both the size of the study and the treatment effect were associated with year of publication. The three main variables that affect the estimate of any future trial are the estimate from the meta-analysis of previous trials prior to the trial, the year difference in the meta-analysis, and the year of the trial conduction. The regression analysis showed that an increase of one unit in the point estimate of the historical meta-analysis would lead to an increase in the predicted estimate of future trial on the SMD scale by 0.88. This result suggests the final trial results are 12% smaller than that from the meta-analysis of trials until that point. CONCLUSION The result of this study indicates that assuming constancy of the treatment difference between the active control and placebo can be questioned. It is therefore important to consider the effect of time in estimating the treatment response if indirect comparisons are being used as the basis of a NI limit.
Collapse
|
3
|
Wu Y, Wu Y, Chen J, Chen P. A Quantitative Bias Analysis to Assess Constancy Assumption in Non-Inferiority Trials Using Bayesian Hierarchical Models. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2071979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Ying Wu
- Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Yanpeng Wu
- School of Public Health, Fudan University, Shanghai, China
| | - Jie Chen
- Department of Biometrics, Overland Pharmaceuticals, Dover, DE 19901, USA
| | - Pingyan Chen
- Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, Guangdong 510515, China
| |
Collapse
|
4
|
Hatswell A, Freemantle N, Baio G, Lesaffre E, van Rosmalen J. Summarising salient information on historical controls: A structured assessment of validity and comparability across studies. Clin Trials 2020; 17:607-616. [PMID: 32957804 PMCID: PMC7649932 DOI: 10.1177/1740774520944855] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.
Collapse
Affiliation(s)
- Anthony Hatswell
- Department of Statistical Science, University College London, London, UK.,Delta Hat Limited, Nottingham, UK
| | - Nick Freemantle
- Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Gianluca Baio
- Department of Statistical Science, University College London, London, UK
| | | | | |
Collapse
|
5
|
Abstract
There exists an ever-increasing number of systematic reviews, with or without meta-analysis, in the field of nutrition. Concomitant with this increase is the increased use of such to guide future research as well as both practice and policy-based decisions. Given this increased production and consumption, a need exists to educate both producers and consumers of systematic reviews, with or without meta-analysis, on how to conduct and evaluate high-quality reviews of this nature in nutrition. The purpose of this paper is to try and address this gap. In the present manuscript, the different types of systematic reviews, with or without meta-analyses, are described as well as the description of the major elements, including methodology and interpretation, with a focus on nutrition. It is hoped that this non-technical information will be helpful to producers, reviewers and consumers of systematic reviews, with or without meta-analysis, in the field of nutrition.
Collapse
|
6
|
Jin M, Feng D, Liu G. Bayesian Approaches on Borrowing Historical Data for Vaccine Efficacy Trials. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2020.1736617] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Man Jin
- AbbVie Inc., North Chicago, IL
| | | | | |
Collapse
|
7
|
Schmidli H, Häring DA, Thomas M, Cassidy A, Weber S, Bretz F. Beyond Randomized Clinical Trials: Use of External Controls. Clin Pharmacol Ther 2019; 107:806-816. [DOI: 10.1002/cpt.1723] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 11/07/2019] [Indexed: 12/30/2022]
|
8
|
Galwey NW. Supplementation of a clinical trial by historical control data: is the prospect of dynamic borrowing an illusion? Stat Med 2016; 36:899-916. [DOI: 10.1002/sim.7180] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 10/19/2016] [Accepted: 11/01/2016] [Indexed: 12/13/2022]
Affiliation(s)
- N. W. Galwey
- GlaxoSmithKline R&D; Medicines Research Centre; Stevenage Hertfordshire U.K
| |
Collapse
|
9
|
|
10
|
Walley R, Sherington J, Rastrick J, Detrait E, Hanon E, Watt G. Using Bayesian analysis in repeated preclinicalin vivostudies for a more effective use of animals. Pharm Stat 2016; 15:277-85. [DOI: 10.1002/pst.1748] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
| | - John Sherington
- Statistical contractor, working for UCB Pharma; 208 Bath Road SL1 3WE Slough, Berks UK
| | - Joe Rastrick
- UCB Pharma; 208 Bath Road Slough, Berks SL1 3WE UK
| | - Eric Detrait
- UCB BioPharma s.p.r.l. Neuroscience Therapeutic Area; Chemin du Foriest Braine-l'Alleud Belgium
| | - Etienne Hanon
- UCB BioPharma s.p.r.l. Neuroscience Therapeutic Area; Chemin du Foriest Braine-l'Alleud Belgium
| | - Gillian Watt
- UCB Pharma; 208 Bath Road Slough, Berks SL1 3WE UK
| |
Collapse
|
11
|
Efthimiou O, Debray TPA, van Valkenhoef G, Trelle S, Panayidou K, Moons KGM, Reitsma JB, Shang A, Salanti G. GetReal in network meta-analysis: a review of the methodology. Res Synth Methods 2016; 7:236-63. [PMID: 26754852 DOI: 10.1002/jrsm.1195] [Citation(s) in RCA: 202] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 09/30/2015] [Accepted: 11/06/2015] [Indexed: 11/11/2022]
Abstract
Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.
Collapse
Affiliation(s)
- Orestis Efthimiou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.,The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gert van Valkenhoef
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sven Trelle
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.,CTU Bern, Department of Clinical Research, University of Bern, Bern, Switzerland
| | - Klea Panayidou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.,The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.,The Dutch Cochrane Centre, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Georgia Salanti
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | | |
Collapse
|
12
|
Lin J, Gamalo-Siebers M, Tiwari R. Non-inferiority and networks: inferring efficacy from a web of data. Pharm Stat 2015; 15:54-67. [DOI: 10.1002/pst.1729] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 08/26/2015] [Accepted: 10/29/2015] [Indexed: 11/07/2022]
Affiliation(s)
- Junjing Lin
- Department of Statistics and Applied Probability; University of California, Santa Barbara; Santa Barbara CA USA
| | - Margaret Gamalo-Siebers
- Mathematical Statistician, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition; Food and Drug Administration; College Park 20740 MD USA
| | - Ram Tiwari
- Office of Biostatistics; Food and Drug Administration; Silver Spring MD USA
| |
Collapse
|
13
|
Gamalo-Siebers M, Gao A, Lakshminarayanan M, Liu G, Natanegara F, Railkar R, Schmidli H, Song G. Bayesian methods for the design and analysis of noninferiority trials. J Biopharm Stat 2015; 26:823-41. [PMID: 26247350 DOI: 10.1080/10543406.2015.1074920] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.
Collapse
Affiliation(s)
| | - Aijun Gao
- b InVentiv Health Clinical , Princeton , New Jersey , USA
| | - Mani Lakshminarayanan
- c Biotechnology Clinical Development Statistics, Pfizer Inc. , Collegeville , Pennsylvania , USA
| | - Guanghan Liu
- d Merck Sharp & Dohme Corp. , North Wales , Pennsylvania , USA
| | | | - Radha Railkar
- c Biotechnology Clinical Development Statistics, Pfizer Inc. , Collegeville , Pennsylvania , USA
| | | | | |
Collapse
|
14
|
Hampson LV, Whitehead J, Eleftheriou D, Brogan P. Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Stat Med 2014; 33:4186-201. [PMID: 24957522 PMCID: PMC4260127 DOI: 10.1002/sim.6225] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 04/07/2014] [Accepted: 05/17/2014] [Indexed: 11/29/2022]
Abstract
This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare-disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to which opinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.
Collapse
Affiliation(s)
- Lisa V Hampson
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster UniversityLancaster, LA1 4YF, U.K.
| | - John Whitehead
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster UniversityLancaster, LA1 4YF, U.K.
| | - Despina Eleftheriou
- Department of Paediatric Rheumatology, UCL Institute of Child Health30 Guilford Street, London WC1N 1EH, U.K.
| | - Paul Brogan
- Department of Paediatric Rheumatology, UCL Institute of Child Health30 Guilford Street, London WC1N 1EH, U.K.
| |
Collapse
|
15
|
Schmidli H, Gsteiger S, Roychoudhury S, O'Hagan A, Spiegelhalter D, Neuenschwander B. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 2014; 70:1023-32. [PMID: 25355546 DOI: 10.1111/biom.12242] [Citation(s) in RCA: 241] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 06/01/2014] [Accepted: 07/01/2014] [Indexed: 11/27/2022]
Abstract
Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.
Collapse
Affiliation(s)
- Heinz Schmidli
- Statistical Methodology, Development, Novartis Pharma AG, Basel, Switzerland
| | | | | | | | | | | |
Collapse
|
16
|
Nikolakopoulou A, Mavridis D, Salanti G. Using conditional power of network meta-analysis (NMA) to inform the design of future clinical trials. Biom J 2014; 56:973-90. [PMID: 25225031 DOI: 10.1002/bimj.201300216] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 05/09/2014] [Accepted: 05/31/2014] [Indexed: 01/19/2023]
Abstract
Clinical trials are typically designed with an aim to reach sufficient power to test a hypothesis about relative effectiveness of two or more interventions. Their role in informing evidence-based decision-making demands, however, that they are considered in the context of the existing evidence. Consequently, their planning can be informed by characteristics of relevant systematic reviews and meta-analyses. In the presence of multiple competing interventions the evidence base has the form of a network of trials, which provides information not only about the required sample size but also about the interventions that should be compared in a future trial. In this paper we present a methodology to evaluate the impact of new studies, their information size, the comparisons involved, and the anticipated heterogeneity on the conditional power (CP) of the updated network meta-analysis. The methods presented are an extension of the idea of CP initially suggested for a pairwise meta-analysis and we show how to estimate the required sample size using various combinations of direct and indirect evidence in future trials. We apply the methods to two previously published networks and we show that CP for a treatment comparison is dependent on the magnitude of heterogeneity and the ratio of direct to indirect information in existing and future trials for that comparison. Our methodology can help investigators calculate the required sample size under different assumptions about heterogeneity and make decisions about the number and design of future studies (set of treatments compared).
Collapse
Affiliation(s)
- Adriani Nikolakopoulou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, Ioannina, 45110, Greece
| | | | | |
Collapse
|
17
|
Gsponer T, Gerber F, Bornkamp B, Ohlssen D, Vandemeulebroecke M, Schmidli H. A practical guide to Bayesian group sequential designs. Pharm Stat 2013; 13:71-80. [DOI: 10.1002/pst.1593] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Revised: 07/25/2013] [Accepted: 08/01/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Thomas Gsponer
- Institute of Social and Preventive Medicine; University of Bern; Bern Switzerland
| | - Florian Gerber
- Institute of Social and Preventive Medicine; University of Bern; Bern Switzerland
| | - Björn Bornkamp
- Statistical Methodology; Novartis Pharma AG; Basel Switzerland
| | - David Ohlssen
- Statistical Methodology; Novartis Pharmaceuticals Corporation; East Hanover, NJ USA
| | | | - Heinz Schmidli
- Statistical Methodology; Novartis Pharma AG; Basel Switzerland
| |
Collapse
|
18
|
Gamalo MA, Tiwari RC, LaVange LM. Bayesian approach to the design and analysis of non-inferiority trials for anti-infective products. Pharm Stat 2013; 13:25-40. [PMID: 23913880 DOI: 10.1002/pst.1588] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 05/22/2013] [Accepted: 07/09/2013] [Indexed: 11/10/2022]
Abstract
In the absence of placebo-controlled trials, determining the non-inferiority (NI) margin for comparing an experimental treatment with an active comparator is based on carefully selected well-controlled historical clinical trials. With this approach, information on the effect of the active comparator from other sources including observational studies and early phase trials is usually ignored because of the need to maintain active comparator effect across trials. This may lead to conservative estimates of the margin that translate into larger sample-size requirements for the design and subsequent frequentist analysis, longer trial durations, and higher drug development costs. In this article, we provide methodological approaches to determine NI margins that can utilize all relevant historical data through a novel power adjusted Bayesian meta-analysis, with Dirichlet process priors, that puts ordered weights on the amount of information a set of data contributes. We also provide a Bayesian decision rule for the non-inferiority analysis that is based on a broader use of available prior information and a sample-size determination that is based on this Bayesian decision rule. Finally, the methodology is illustrated through several examples.
Collapse
Affiliation(s)
- Meg A Gamalo
- Office of Biostatistics, Food and Drug Administration, Silver Spring, MD, 20993-0002, USA
| | | | | |
Collapse
|
19
|
Naci H, O’Connor AB. Assessing comparative effectiveness of new drugs before approval using prospective network meta-analyses. J Clin Epidemiol 2013; 66:812-6. [DOI: 10.1016/j.jclinepi.2013.04.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Revised: 03/18/2013] [Accepted: 04/12/2013] [Indexed: 11/16/2022]
|
20
|
Donegan S, Williamson P, D'Alessandro U, Tudur Smith C. Assessing key assumptions of network meta-analysis: a review of methods. Res Synth Methods 2013; 4:291-323. [PMID: 26053945 DOI: 10.1002/jrsm.1085] [Citation(s) in RCA: 152] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Revised: 06/11/2013] [Accepted: 06/14/2013] [Indexed: 11/08/2022]
Abstract
BACKGROUND Homogeneity and consistency assumptions underlie network meta-analysis (NMA). Methods exist to assess the assumptions but they are rarely and poorly applied. We review and illustrate methods to assess homogeneity and consistency. METHODS Eligible articles focussed on indirect comparison or NMA methodology. Articles were sought by hand-searching and scanning references (March 2013). Assumption assessment methods described in the articles were reviewed, and applied to compare anti-malarial drugs. RESULTS 116 articles were included. Methods to assess homogeneity were: comparing characteristics across trials; comparing trial-specific treatment effects; using hypothesis tests or statistical measures; applying fixed-effect and random-effects pair-wise meta-analysis; and investigating treatment effect-modifiers. Methods to assess consistency were: comparing characteristics; investigating treatment effect-modifiers; comparing outcome measurements in the referent group; node-splitting; inconsistency modelling; hypothesis tests; back transformation; multidimensional scaling; a two-stage approach; and a graph-theoretical method. For the malaria example, heterogeneity existed for some comparisons that was unexplained by investigating treatment effect-modifiers. Inconsistency was detected using node-splitting and inconsistency modelling. It was unclear whether the covariates explained the inconsistency. CONCLUSIONS Presently, we advocate applying existing assessment methods collectively to gain the best understanding possible regarding whether assumptions are reasonable. In our example, consistency was questionable; therefore the NMA results may be unreliable.
Collapse
Affiliation(s)
- Sarah Donegan
- Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK
| | - Paula Williamson
- Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK
| | - Umberto D'Alessandro
- Department of Parasitology, Prince Leopold Institute of Tropical Medicine, National estraat 155, B-2000, Antwerp, Belgium
| | - Catrin Tudur Smith
- Department of Biostatistics, Faculty of Health & Life Sciences, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool, L69 3GS, UK
| |
Collapse
|
21
|
Gsteiger S, Neuenschwander B, Mercier F, Schmidli H. Using historical control information for the design and analysis of clinical trials with overdispersed count data. Stat Med 2013; 32:3609-22. [DOI: 10.1002/sim.5851] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2012] [Revised: 04/16/2013] [Accepted: 04/23/2013] [Indexed: 01/17/2023]
|
22
|
Witte S, Schmidli H, O'Hagan A, Racine A. Designing a non-inferiority study in kidney transplantation: a case study. Pharm Stat 2011; 10:427-32. [DOI: 10.1002/pst.511] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Revised: 07/19/2011] [Accepted: 07/19/2011] [Indexed: 01/05/2023]
|