ArticleBayesian analysis2021
A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes.
Article in Bayesian analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity.Frontiers in oncology · 2023Pooled it
- A Biomarker-Based Dose-Schedule Optimization Design for Immunotherapy Trials.Statistics in medicine · 2026Article
- Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.Clinical trials (London, England) · 2024Review
- Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses.Clinical trials (London, England) · 2024Review
- Local continual reassessment methods for dose finding and optimization in drug-combination trials.Statistical methods in medical research · 2023Article
- Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.Frontiers in pharmacology · 2023Review
- Bayesian modelling strategies for borrowing of information in randomised basket trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- Precision Bayesian phase I-II dose-finding based on utilities tailored to prognostic subgroups.Statistics in medicine · 2021Article
- A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called "baskets", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.
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Registered trials
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