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Zanbar L, Lev S, Faran Y. Can Physical, Psychological, and Social Vulnerabilities Predict Ageism? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:171. [PMID: 36612491 PMCID: PMC9819222 DOI: 10.3390/ijerph20010171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Ageism can be expressed as the discrimination, social exclusion, and even abuse of older adults. The literature suggests that certain vulnerabilities could be risk factors affecting people's ageism. Based on the Social Identity Theory, the present study aimed to examine the association of physical/psychological and social vulnerabilities with ageism. The sample consisted of 200 Israelis from the general population who completed self-report questionnaires. Hierarchical regression indicated that low well-being, high post-traumatic distress, and limited social support were associated with ageism. Furthermore, the association of post-traumatic distress with ageism increased with age. The findings expand the knowledge of vulnerabilities as risk factors for ageism, perhaps reflecting its unconscious nature, and can assist in designing interventions for people interacting with older adults.
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Affiliation(s)
- Lea Zanbar
- School of Social Work, Ariel University, Ariel 40700, Israel
- Faculty of Social Work, Ashkelon Academic College, Ashkelon 78211, Israel
| | - Sagit Lev
- School of Social Work, Bar Ilan University, Ramat-Gan 52900, Israel
| | - Yifat Faran
- Faculty of Social Work, Ashkelon Academic College, Ashkelon 78211, Israel
- Department of Special Education, Hemdat Hadarom College Sdot Hanegev Regional Council, Netivot 8771302, Israel
- Department of Gerontology, Ben Gurion University, Be’er Sheva 8410501, Israel
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Herrmann C, Kluge C, Pilz M, Kieser M, Rauch G. Improving sample size recalculation in adaptive clinical trials by resampling. Pharm Stat 2021; 20:1035-1050. [PMID: 33792167 DOI: 10.1002/pst.2122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 12/16/2020] [Accepted: 03/08/2021] [Indexed: 11/11/2022]
Abstract
Sample size calculations in clinical trials need to be based on profound parameter assumptions. Wrong parameter choices may lead to too small or too high sample sizes and can have severe ethical and economical consequences. Adaptive group sequential study designs are one solution to deal with planning uncertainties. Here, the sample size can be updated during an ongoing trial based on the observed interim effect. However, the observed interim effect is a random variable and thus does not necessarily correspond to the true effect. One way of dealing with the uncertainty related to this random variable is to include resampling elements in the recalculation strategy. In this paper, we focus on clinical trials with a normally distributed endpoint. We consider resampling of the observed interim test statistic and apply this principle to several established sample size recalculation approaches. The resulting recalculation rules are smoother than the original ones and thus the variability in sample size is lower. In particular, we found that some resampling approaches mimic a group sequential design. In general, incorporating resampling of the interim test statistic in existing sample size recalculation rules results in a substantial performance improvement with respect to a recently published conditional performance score.
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Affiliation(s)
- Carolin Herrmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Corinna Kluge
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Maximilian Pilz
- Institute of Medical Biometry and Informatics, University Medical Center Ruprechts-Karls University Heidelberg, Heidelberg, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics, University Medical Center Ruprechts-Karls University Heidelberg, Heidelberg, Germany
| | - Geraldine Rauch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117 Berlin, Germany
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Xie T, Zhang P, Shih WJ, Tu Y, Lan KKG. Dynamic Monitoring of Ongoing Clinical Trials. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1880965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Tai Xie
- Department of Biostatistics and Programming, Brightech International, Somerset, NJ
| | - Peng Zhang
- Department of Biostatistics and Programming, Brightech International, Somerset, NJ
| | - Weichung Joe Shih
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, The State University of New Jersey, Piscataway, NJ
| | - Yue Tu
- Department of Biostatistics and Programming, Brightech International, Somerset, NJ
| | - K. K. Gordon Lan
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, The State University of New Jersey, Piscataway, NJ
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Herrmann C, Rauch G. Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs. Methods Inf Med 2021; 60:1-8. [PMID: 33648007 PMCID: PMC8432271 DOI: 10.1055/s-0040-1721727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background
An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.
This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. Objectives
Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation.
Methods
Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a “jump” from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components.
Results
We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes.
Conclusion
Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.
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Affiliation(s)
- Carolin Herrmann
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Berlin, Germany
| | - Geraldine Rauch
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Berlin, Germany
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Edwards JM, Walters SJ, Kunz C, Julious SA. A systematic review of the "promising zone" design. Trials 2020; 21:1000. [PMID: 33276810 PMCID: PMC7718653 DOI: 10.1186/s13063-020-04931-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 11/25/2020] [Indexed: 12/01/2022] Open
Abstract
Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.
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Affiliation(s)
- Julia M Edwards
- School of Health and Related Research, The University of Sheffield, Sheffield, UK.
| | - Stephen J Walters
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
| | - Cornelia Kunz
- Boehringer Ingelheim, Biberach an der Riss, Biberach, Germany
| | - Steven A Julious
- School of Health and Related Research, The University of Sheffield, Sheffield, UK
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Grayling MJ, Mander AP. Do single-arm trials have a role in drug development plans incorporating randomised trials? Pharm Stat 2016; 15:143-51. [PMID: 26609689 PMCID: PMC4855632 DOI: 10.1002/pst.1726] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 07/14/2015] [Accepted: 10/19/2015] [Indexed: 12/31/2022]
Abstract
Often, single-arm trials are used in phase II to gather the first evidence of an oncological drug's efficacy, with drug activity determined through tumour response using the RECIST criterion. Provided the null hypothesis of 'insufficient drug activity' is rejected, the next step could be a randomised two-arm trial. However, single-arm trials may provide a biased treatment effect because of patient selection, and thus, this development plan may not be an efficient use of resources. Therefore, we compare the performance of development plans consisting of single-arm trials followed by randomised two-arm trials with stand-alone single-stage or group sequential randomised two-arm trials. Through this, we are able to investigate the utility of single-arm trials and determine the most efficient drug development plans, setting our work in the context of a published single-arm non-small-cell lung cancer trial. Reference priors, reflecting the opinions of 'sceptical' and 'enthusiastic' investigators, are used to quantify and guide the suitability of single-arm trials in this setting. We observe that the explored development plans incorporating single-arm trials are often non-optimal. Moreover, even the most pessimistic reference priors have a considerable probability in favour of alternative plans. Analysis suggests expected sample size savings of up to 25% could have been made, and the issues associated with single-arm trials avoided, for the non-small-cell lung cancer treatment through direct progression to a group sequential randomised two-arm trial. Careful consideration should thus be given to the use of single-arm trials in oncological drug development when a randomised trial will follow.
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Affiliation(s)
| | - Adrian P. Mander
- MRC Biostatistics Unit Hub for Trials Methodology ResearchCambridgeUK
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Pritchett YL, Menon S, Marchenko O, Antonijevic Z, Miller E, Sanchez-Kam M, Morgan-Bouniol CC, Nguyen H, Prucka WR. Sample Size Re-estimation Designs In Confirmatory Clinical Trials—Current State, Statistical Considerations, and Practical Guidance. Stat Biopharm Res 2015. [DOI: 10.1080/19466315.2015.1098564] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Abstract
Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative - causing one's estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao-Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature.
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Affiliation(s)
- Jack Bowden
- 1 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,2 MRC Biostatistics Unit, Cambridge, UK
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Klinglmueller F, Posch M, Koenig F. Adaptive graph-based multiple testing procedures. Pharm Stat 2014; 13:345-56. [PMID: 25319733 PMCID: PMC4789493 DOI: 10.1002/pst.1640] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 08/13/2014] [Accepted: 08/26/2014] [Indexed: 11/17/2022]
Abstract
Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations.
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Affiliation(s)
- Florian Klinglmueller
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
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