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Optimal Bayesian adaptive trials when treatment efficacy depends on biomarkers
Yifan Zhang1, Lorenzo Trippa1,2, Giovanni Parmigiani1,2
1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, U.S.A.
Biometrics
|November 18, 2015
Summary
This study introduces optimal biomarker-integrated adaptive trial designs for precision medicine, maximizing patient responses. These novel designs offer a benchmark for evaluating adaptive clinical trial strategies in personalized medicine.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Precision Medicine
Background:
- Clinical biomarkers are crucial in precision medicine and cancer clinical trials.
- Adaptive trial designs use early patient data for later treatment decisions.
- Existing optimal adaptive designs do not incorporate biomarkers.
Purpose of the Study:
- To mathematically derive optimal biomarker-integrated adaptive trial designs.
- To maximize expected trial utility, focusing on patient responses within a defined horizon.
- To establish a theoretical benchmark for evaluating adaptive trial designs in personalized medicine.
Main Methods:
- Developed mathematical steps for computing optimal biomarker-integrated adaptive trial designs.
- Focused on maximizing expected patient responses given a utility function.
- Compared the optimal design's performance against Bayesian Adaptive Randomization (BAR) and marker-stratified balanced randomization (BR).
Main Results:
- The optimal design maximizes expected trial utility.
- The performance difference between BAR and optimal designs is minimal with imbalanced biomarker subgroups.
- Marker-stratified balanced randomization (BR) approaches near-optimal expected utility for two-treatment comparisons with a large patient horizon.
Conclusions:
- Biomarker integration enhances adaptive trial designs for precision medicine.
- The proposed optimal design serves as a benchmark for adaptive clinical trials.
- Marker-stratified balanced randomization is a competitive strategy for certain trial scenarios.
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