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

Area of Science:

• Behavioral Science
• Health Intervention Science
• Biostatistics

Background:

• Current intervention optimization relies on component screening approach (CSA) in factorial trials.
• CSA involves identifying significant main effects and interactions based on fixed thresholds for component selection.

Purpose of the Study:

• To introduce and evaluate a posterior expected value approach for intervention component selection.
• To compare the performance of the posterior expected value approach against CSA and traditional benchmarks.

Main Methods:

• Monte Carlo simulations were used to assess the performance of the posterior expected value approach and CSA.
• Evaluated approaches against random selection and classical treatment package benchmarks.
• Simulations incorporated realistic variations in factorial optimization trials.

Main Results:

• Both posterior expected value approach and CSA demonstrated substantial performance gains over benchmarks.
• The posterior expected value approach showed modest but consistent superiority over CSA in accuracy, sensitivity, and specificity.
• Performance improvements were observed across various simulated trial conditions.

Conclusions:

• The posterior expected value approach offers a more accurate and extensible method for intervention optimization within the multiphase optimization strategy (MOST).
• This Bayesian decision theory-based method provides practical advantages for selecting intervention components in factorial trials.
• Future research should explore further applications of the posterior expected value approach in intervention design and decision-making.