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A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials
Laurien M Buffart1, Andrea Bassi2, Martijn M Stuiver3
1Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Journal of Clinical Epidemiology
|May 28, 2023
Summary
A Bayesian-adaptive design significantly reduced sample size in an exercise oncology trial. This approach identified the optimal exercise intervention more efficiently than traditional methods.
Area of Science:
- Clinical Trials
- Oncology
- Exercise Science
Background:
- Adaptive trial designs can minimize sample sizes and costs.
- Exercise interventions are being explored to mitigate chemotherapy side effects.
Purpose of the Study:
- To illustrate a Bayesian-adaptive decision-theoretic design in a multiarm exercise oncology trial.
- To compare the efficiency of adaptive designs versus traditional methods for identifying effective interventions.
Main Methods:
- Reanalyzed the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial data using Bayesian-adaptive and frequentist group-sequential methods.
- Incorporated interim analyses and evaluated different Bayesian settings, including arm dropping and 'pick-the-winner' scenarios.
- Endpoint: chemotherapy treatment modifications (any vs. none).
Main Results:
- The Bayesian-adaptive design identified the supervised exercise (OnTrack) as most effective earlier than a frequentist approach.
- OnTrack showed significantly fewer chemotherapy treatment modifications (12%) compared to usual care (34%).
- The Bayesian approach, particularly 'pick-the-winner', substantially reduced the required sample size.
Conclusions:
- Bayesian-adaptive decision-theoretic designs offer significant sample size reductions for multiarm oncology trials.
- This adaptive approach enhances the efficiency of identifying superior exercise interventions during chemotherapy.
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