Abstract
In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2k 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 approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST.
Original language | English (US) |
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Pages (from-to) | 656-678 |
Number of pages | 23 |
Journal | Psychological Methods |
Volume | 29 |
Issue number | 4 |
DOIs | |
State | Published - Apr 13 2023 |
Keywords
- Bayesian decision analytics
- decision-making
- factorial optimization trial
- intervention optimization
- multiphase optimization strategy
ASJC Scopus subject areas
- Psychology (miscellaneous)