Robust exchangeability designs for early phase clinical trials with multiple strata

Neuenschwander, Beat; Wandel, Simon; Roychoudhury, Satrajit; Bailey, Stuart

doi:10.1002/pst.1730

Cited by 127 publications

(160 citation statements)

References 45 publications

(69 reference statements)

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“…For instance, when between treatment class heterogeneity is relatively large or there is a treatment class with distinctly different pattern, P‐EX model has the advantage of avoiding the excessive borrowing of information, as illustrated in the second design of the simulation study. All the above illustrate the benefits of partial exchangeability, as described by Neuenschwander et al in their work. Subgroup analysis using the standard model is a simple approach which performs well when there are sufficient data available for each treatment class, but it produces estimates with higher bias and uncertainty when data within a treatment class are limited.…”

Section: Discussionmentioning

confidence: 99%

Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

Papanikos

Thompson

Abrams

et al. 2020

Statistics in Medicine

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Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final clinical outcome and to predict clinical benefit or harm. Such endpoints are assessed for their predictive value of clinical benefit by investigating the surrogate relationship between treatment effects on the surrogate and final outcomes using meta‐analytic methods. When surrogate relationships vary across treatment classes, such validation may fail due to limited data within each treatment class. In this paper, two alternative Bayesian meta‐analytic methods are introduced which allow for borrowing of information from other treatment classes when exploring the surrogacy in a particular class. The first approach extends a standard model for the evaluation of surrogate endpoints to a hierarchical meta‐analysis model assuming full exchangeability of surrogate relationships across all the treatment classes, thus facilitating borrowing of information across the classes. The second method is able to relax this assumption by allowing for partial exchangeability of surrogate relationships across treatment classes to avoid excessive borrowing of information from distinctly different classes. We carried out a simulation study to assess the proposed methods in nine data scenarios and compared them with subgroup analysis using the standard model within each treatment class. We also applied the methods to an illustrative example in colorectal cancer which led to obtaining the parameters describing the surrogate relationships with higher precision.

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Section: Discussionmentioning

confidence: 99%

Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

Papanikos

Thompson

Abrams

et al. 2020

Statistics in Medicine

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“…If this assumption is questionable, one should carefully decide whether the proposed design is really appropriate. Additionally, more sophisticated statistical models,10 37 which automatically adapt to the situation when there is a substantial difference between the results of the actual study and the available evidence, may then be needed.…”

Section: Discussionmentioning

confidence: 99%

Designing and analysing clinical trials in mental health: an evidence synthesis approach

Wandel

Roychoudhury

2016

Evid Based Mental Health

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Objective When planning a clinical study, evidence on the treatment effect is often available from previous studies. However, this evidence is mostly ignored for the analysis of the new study. This is unfortunate, since using it could lead to a smaller study without compromising power. We describe a design that addresses this issue. Methods We use a Bayesian meta-analytic model to incorporate the available evidence in the analysis of the new study. The shrinkage estimate for the new study integrates the evidence from the other studies. At the planning phase of the study, it allows a statistically justified reduction of the sample size. Results The design is illustrated using data from an Food and Drug Administration (FDA) review of lurasidone for the treatment of schizophrenia. Three studies inform the meta-analysis before the new study is conducted. Results from an additional phase III study, which were not available at the time of the FDA review, are then used for the actual analysis. Conclusions In the presence of reliable and relevant evidence, the design offers a way to conduct a smaller study without compromising power. It therefore fills a gap between the assessment of evidence and its actual use in the design and analysis of studies. INTRODUCTIONClinical trials are a cornerstone of evidence-based medicine. The impressive number of more than 220 000 registered trials in the electronic database http://www.clinicaltrials.gov supports this statement, and further growth at an accelerated rate is likely. While this number shows the importance of clinical research, it also reveals that typically more than one clinical study investigating the same treatment (or interventions) is conducted. When designing a clinical study, it is therefore good practice to review the literature 1 and assess the body of evidence. This will provide information on the previous experience in the field and relevant topics such as commonly used end points, challenges with missing data and expected patient enrolment. It may also undermine the need for a new study.1 2 The latter is specifically important since resources to fund studies are limited and competition is high, whether in academia or industry. While all of the preceding reasons justify the need to conduct a systematic review before starting a new study, this is even more critical for statistical reasons. Defining end points, analysis sets, primary and secondary analyses and, most importantly, the sample size calculation critically depends on realistic and reliable assumptions. However, for all of these tasks, the information (data) from the review is used only indirectly in the new study. For example, an estimated treatment effect from a meta-analysis can serve as the base for the sample size calculation, yet it will not contribute to the actual analysis of the study. This is unfortunate, since it means that in the actual analysis, we ignore what we know already. Using the information in the analysis, too, could lead to a smaller study or to higher power. The question t...

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“…We note in passing that the model used here is different from the EXNEX model by Neuenschwander et al (2015). The mixture model we use here is a mixture of random variables that allows to explicitly write down the posterior distribution, whereas EXNEX is described as a mixture of densities that leads to a posterior that is not multivariate normal.…”

Section: Comparison Of Simultaneous Inference With Bayesian Shrinkagementioning

confidence: 99%

Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups

Glimm

2018

Biometrical J

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This paper discusses a number of methods for adjusting treatment effect estimates in clinical trials where differential effects in several subpopulations are suspected. In such situations, the estimates from the most extreme subpopulation are often overinterpreted. The paper focusses on the construction of simultaneous confidence intervals intended to provide a more realistic assessment regarding the uncertainty around these extreme results. The methods from simultaneous inference are compared with shrinkage estimates arising from Bayesian hierarchical models by discussing salient features of both approaches in a typical application. K E Y W O R D Sselection bias, shrinkage estimation, simultaneous confidence intervals, subpopulations

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Robust exchangeability designs for early phase clinical trials with multiple strata

Cited by 127 publications

References 45 publications

Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

Designing and analysing clinical trials in mental health: an evidence synthesis approach

Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups

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