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BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.

ABSTRACT: A Bayesian group sequential design is proposed that performs survival comparisons within patient subgroups in randomized trials where treatment-subgroup interactions may be present. A latent subgroup membership variable is assumed to allow the design to adaptively combine homogeneous subgroups, or split heterogeneous subgroups, to improve the procedure's within-subgroup power. If a baseline covariate related to survival is available, the design may incorporate this information to improve subgroup identification while basing the comparative test on the average hazard ratio. General guidelines are provided for calibrating prior hyperparameters and design parameters to control the overall Type I error rate and optimize performance. Simulations show that the design is robust under a wide variety of different scenarios. When two or more subgroups are truly homogeneous but differ from the other subgroups, the proposed method is substantially more powerful than tests that either ignore subgroups or conduct a separate test within each subgroup. Supplementary materials for this article are available online.

SUBMITTER: Lin R

PROVIDER: S-EPMC9109248 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.

Lin Ruitao R Thall Peter F PF Yuan Ying Y

Journal of the American Statistical Association 20201130 533

A Bayesian group sequential design is proposed that performs survival comparisons within patient subgroups in randomized trials where treatment-subgroup interactions may be present. A latent subgroup membership variable is assumed to allow the design to adaptively combine homogeneous subgroups, or split heterogeneous subgroups, to improve the procedure's within-subgroup power. If a baseline covariate related to survival is available, the design may incorporate this information to improve subgrou ...[more]

PMID: 35582047

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Project description:BackgroundThe integration of historical data into ongoing clinical trials through Bayesian Dynamic Borrowing offers significant advantages, including reduced sample size, trial duration, and associated costs. However, challenges such as ensuring exchangeability between historical and current data and mitigating Type I error inflation remain critical. This study proposes a Bayesian group-sequential design incorporating a Self-Adaptive Mixture (SAM) prior framework to address these challenges in medical device trials.MethodsThe SAM prior combines informative priors derived from historical data with weakly informative priors, dynamically adjusting the weight of historical information based on congruence with current trial data. The design includes interim analyses, with Bayesian decision rules leveraging futility and efficacy boundaries derived using the frequentist spending functions. Effective Sample Size calculations informed adjustments to sample size and allocation ratios between experimental and control arms at each interim. The methodology was evaluated using a motivating example from a cardiovascular device trial with a noninferiority hypothesis.ResultsFour historical studies with substantial heterogeneity were incorporated. The SAM prior showed improved adaptation to prior-data conflicts compared to static methods, maintaining Type I error and Power at their nominal levels. In the motivating trial, the MAP prior was approximated as a mixture of beta distributions, facilitating congruence testing and posterior inference. Simulation studies confirmed the proposed design's efficiency under both congruent and incongruent scenarios.ConclusionsThe proposed Bayesian Group-Sequential Design with SAM prior offers a robust, adaptive framework for medical device trials, balancing statistical rigor with clinical interpretability. This approach enhances decision-making and supports timely, cost-effective evaluations, particularly in dynamic contexts like medical device development.

Dataset Information

BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.

Publications

BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.

Similar Datasets

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