Bayesian Treatment Screening and Selection Using Subgroup-Specific Utilities of Response and Toxicity
Abstract
A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 16, 2022
- Source ID
- 10.1111/biom.13738
Entities
People
- Juhee Lee
- Pavlos Msaouel
- Peter F Thall
Organizations
- American Society of Clinical Oncology
- Kidney Cancer Association
- National Cancer Institute
- National Science Foundation
- The University of Texas MD Anderson Cancer Center
- United States Department of Defense
- University of California, Santa Cruz