A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints.
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| Title: | A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints. |
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| Authors: | Han, Yueyang1 (AUTHOR), Shi, Haolun1 (AUTHOR) haoluns@sfu.ca |
| Source: | Statistics in Biopharmaceutical Research. Jul-Sep2026, Vol. 18 Issue 3, p436-447. 12p. |
| Subjects: | Bayesian analysis, Survival rate, Treatment effectiveness, Biomarkers, Tumor classification, Clinical trials |
| Abstract: | We propose a basket trial design that addresses patient heterogeneity in treatment effect across subgroups and evaluates the effectiveness of treatments with a survival endpoint via Bayesian latent subgroup analysis. In basket trials, various cancer types are enrolled, yet the response to treatment can vary across these types. To accommodate this variability, our design assumes cancer types may belong to either a sensitive subgroup, responsive to treatment, or an insensitive subgroup, unresponsive to treatment. During interim analyses, Bayesian subgroup analysis is conducted to classify the cancer types into different clusters according to both the survival time and the longitudinal biomarker measurements of the patient. Finally, we make Bayesian inferences to decide whether to stop recruiting patients for each cluster early and assess whether the treatment is effective for each cluster according to the estimated median survival time. The simulation study shows that our proposed method performs better than the independent approach and the Bayesian Hierarchical Modeling (BHM) method in most of the scenarios, with higher power to detect treatment effects in sensitive cancer types while maintaining the desired type I error rate for insensitive types. This integrated approach holds promise for optimizing treatment efficacy across diverse cancer populations with varying characteristics. [ABSTRACT FROM AUTHOR] |
| Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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