Structural Nested Mean Model Estimation Using Inverse-Weighted Regression with Residuals
ResearchPosted on rand.org 2012Published In: The Methodology Center Technical Report Series, no. 12-121 (State College, PA: The Methodology Center, 2012), 44 p
This article considers the problem of examining causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and putative confounders are time-varying. Robins' (1994) structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and candidate time-varying moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the candidate time-varying moderators of interest are the sole time-varying confounders. The proposed IPTW+RR approach identifies the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured putative time-varying confounders, which are not candidate time-varying moderators of interest. A small simulation experiment is used to compare IPTW+RR vs the traditional regression approach, and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. The methodology is illustrated in a case study examining the moderated time-varying effects of additional adolescent substance use treatment on future substance use, as a function of time-varying frequency of substance use.
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