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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase

Jillian C Strayhorn1, Charles M Cleland2, David J Vanness3

  • 1Department of Social and Behavioral Sciences, School of Global Public Health, New York University.

Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association
|August 3, 2023
PubMed
Summary
This summary is machine-generated.

Decision analysis for intervention value efficiency (DAIVE) helps optimize behavioral interventions by balancing effectiveness and implementability. This framework tailors intervention components to diverse decision-maker preferences for better outcomes.

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Area of Science:

  • Behavioral Science
  • Health Intervention Science
  • Decision Analysis

Background:

  • Optimizing multicomponent interventions is complex, requiring balancing effectiveness and implementability.
  • Diverse decision-maker preferences and multiple outcomes complicate intervention selection.
  • Advances in the multiphase optimization strategy (MOST) enable preference-based intervention optimization.

Purpose of the Study:

  • Introduce Decision Analysis for Intervention Value Efficiency (DAIVE), a novel framework for MOST.
  • Apply DAIVE to select optimized interventions using empirical data from a factorial trial.
  • Demonstrate how DAIVE accommodates varied decision-maker objectives and preferences.

Main Methods:

  • Defined hypothetical decision-maker preferences.
  • Applied the DAIVE framework to identify optimized interventions for each preference set.
  • Utilized data from a factorial optimization trial.

Main Results:

  • DAIVE effectively guides decisions on optimized intervention composition.
  • The selection of optimized interventions varied based on decision-maker preferences and objectives.
  • Illustrates the impact of individual preferences on intervention design.

Conclusions:

  • DAIVE provides a structured approach for intervention scientists to optimize interventions.
  • Offers practical recommendations for applying DAIVE with factorial trial data.
  • Facilitates the development of more tailored and effective behavioral interventions.