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Abstract:
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In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have been growing in popularity, and sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has recently increased, their design has remained mainly limited to the frequentist setting, which may not fully account for uncertainty in design parameters. Specifically, standard frequentist formulae rely on several assumptions that can be easily misspecified. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this presentation, we show how Bayesian calculations allow more realistic and robust estimates that account for uncertainty in inputs while relying on fewer assumptions through the “two priors” approach. We evaluate the proposed methodology in a simulation study, and we implement it to estimate the sample size for a full-scale SMART of an Internet-Based Adaptive Stress Management intervention based on a pilot SMART conducted on cardiovascular disease patients.
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