Bayesian cancer clinical trial designs with subgroup-specific decisions

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model.

Original languageEnglish (US)
Article number105860
JournalContemporary Clinical Trials
Volume90
DOIs
StatePublished - Mar 2020

Keywords

  • Adaptive design
  • Bayesian design
  • Dose finding
  • Group sequential design
  • Phase I-II clinical trial
  • Precision medicine
  • Utility function

ASJC Scopus subject areas

  • Pharmacology (medical)

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

Fingerprint

Dive into the research topics of 'Bayesian cancer clinical trial designs with subgroup-specific decisions'. Together they form a unique fingerprint.

Cite this