ArticleJournal of the American Statistical Association2021
BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- An Adaptive Biomarker-based Umbrella Trial Design Using Bayesian Latent Class Model.Statistics in biopharmaceutical research · 2026Article
- A Bayesian Adaptive Marker-Stratified Design for Phase II Clinical Trials Using Calibrated Spike-and-Slab priors.Statistics in biopharmaceutical research · 2026Article
- BIPSE: A biomarker-based phase I/II design for immunotherapy trials with progression-free survival endpoint.Statistics in medicine · 2022Article
- A Problematic Biomarker Trial Design.Journal of the National Cancer Institute · 2022Article
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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