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Bayesian enrichment strategies for randomized discontinuation trials.
Lorenzo Trippa1, Gary L Rosner, Peter Müller
1Harvard School of Public Health and Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. lorenzo.trippa@jimmi.harvard.com
Biometrics
|July 1, 2011
Summary
This study introduces an optimal Bayesian approach for designing random discontinuation trials (RDTs) in oncology. It helps select key trial parameters to efficiently evaluate new cancer treatments.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Oncology Research
Background:
- Random discontinuation designs (RDDs) are used in clinical trials.
- Optimizing RDD parameters is crucial for efficient drug evaluation, especially for cytostatic agents in oncology.
Purpose of the Study:
- To propose an optimal selection of design parameters for RDDs using a Bayesian decision-theoretic framework.
- To apply this framework to oncology phase II studies for evaluating cytostatic agents.
Main Methods:
- A two-stage RDD approach: preliminary open-label stage to identify a subpopulation, followed by a randomized stage comparing new vs. control treatment within the subgroup.
- Utilizing a Bayesian decision-theoretic approach with a defined probability model for tumor growth and a utility function.
- Developing a computational procedure for optimal selection of tuning parameters (e.g., patient numbers, stage durations).
Main Results:
- The study outlines a method for optimizing critical design parameters in RDDs.
- This approach allows for the selection of sensitive subpopulations for targeted treatment evaluation.
- The computational procedure facilitates the determination of optimal trial parameters.
Conclusions:
- The proposed Bayesian decision-theoretic approach provides an optimal method for selecting design parameters in RDDs.
- This methodology is particularly relevant for phase II oncology trials evaluating cytostatic agents.
- The framework enables efficient identification of responsive patient subgroups and optimizes trial resource allocation.
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