A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science.

ABSTRACT: In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2^k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value

SUBMITTER: Strayhorn JC 

PROVIDER: S-EPMC12701551 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature