Using factorial mediation analysis to better understand the effects of interventions

Abstract

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Keywords: Factorial experiment; Mediation analysis; Multiphase optimization strategy; Optimization trial.

Fig 1

Example single mediator model with all main and interaction effects, Opt-IN. Note. Some two-way, three-way, and four-way interaction effects are omitted from this figure for simplicity. Shading emphasizes the mediation of the BUDDY × PCP × TEXT interaction effect.