Methods for Causal Effects of Multicomponent Interventions in Longitudinal Studies with Interference

Thomas M. Menino Convention & Exhibition Center 

Spillover effects arise when an intervention received by one unit affects the outcomes of units within a predefined group, referred to as an interference set. Such effects commonly occur in clusters/sociometric networks. HIV prevention programs are often delivered to communities as intervention packages that contain multiple components. Disentangling component-specific effects of intervention packages is essential for fully understanding the effectiveness of HIV interventions. However, existing causal methods for estimating spillover effects are typically limited to settings with a single intervention, or multiple interventions that are analyzed as a whole, thereby unable to provide insights into which components were driving (or hindering) the effectiveness. Here, we develop novel causal methods for time-varying exposure to intervention packages with interference. We expand partial interference assumption, use marginal structure models to estimate the potential outcome, with time-updated inverse probability weighting to adjust for confounders. Generalized estimating equations is employed to derive closed-form robust variance estimators.

Spillover effect

intervention package

time-varying exposure

marginal structure model 

Section on Statistics in Epidemiology