A Phase I–II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes

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2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)179-202
Number of pages24
JournalBayesian Analysis
Volume16
Issue number1
DOIs
StatePublished - 2021

Keywords

  • adaptive randomization
  • basket trial
  • Bayesian design
  • missing data
  • optimal treatment regime
  • phase I–II clinical trial

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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