ArticleStatistics in biopharmaceutical research2026
An Adaptive Biomarker-based Umbrella Trial Design Using Bayesian Latent Class Model.
Article in Statistics in biopharmaceutical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
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Authors and funding
5 authors.
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Abstract
A Bayesian adaptive umbrella trial design is proposed to identify effective treatments for biomarker-defined subgroups. This design employs a mixture regression model to jointly model short-term binary and long-term time-to-event outcomes and introduces a latent class model to adaptively cluster subgroups with similar treatment-outcome distributions. The model aims to dynamically borrow information between subgroups in the same class, enhancing the efficiency and robustness in testing subgroup-specific treatment effects. To strengthen the individual ethics of the trial, the design incorporates response-adaptive randomization with early stopping rules for futility and superiority. The simulation results confirm that the proposed design demonstrates desired performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.