Subgroups Analysis when Treatment and Moderators are Time-varying

Prevention scientists are often interested in understanding characteristics of participants that are predictive of treatment
effects because these characteristics can be used to inform the types of individuals who benefit more or less from treatment
or prevention programs. Often, effect moderation questions are examined using subgroups analysis or, equivalently, using covariate
× treatment interactions in the context of regression analysis. This article focuses on conceptualizing and examining causal
effect moderation in longitudinal settings in which both treatment and the putative moderators are time-varying. Studying
effect moderation in the time-varying setting helps identify which individuals will benefit more or less from additional treatment
services on the basis of both individual characteristics and their evolving outcomes, symptoms, severity, and need. Examining
effect moderation in these longitudinal settings, however, is difficult because moderators of future treatment may themselves
be affected by prior treatment (for example, future moderators may be mediators of prior treatment). This article introduces
moderated intermediate causal effects in the time-varying setting, describes how they are part of Robins’ Structural Nested
Mean Model, discusses two problems with using a traditional regression approach to estimate these effects, and describes a
new approach (a two-stage regression estimator) to estimate these effects. The methodology is illustrated using longitudinal
data to examine the time-varying effects of receiving community-based substance abuse treatment as a function of time-varying
severity (or need).