Journal of Data Science

Abstract: It is known that “standard methods for estimating the causal effect of a time-varying treatment on the mean of a repeated measures outcome (for example, GEE regression) may be biased when there are time-dependent variables that are simultaneously confounders of the effect of interest and are predicted by previous treatment” (Hern´an et al. 2002). Inverse-probability of treatment weighted (IPTW) methods are developed in the literature of causal inference. In genetic studies, however, the main interest is to estimate or test the genetic effect rather than the treatment effect. In this work, we describe an IPTW method that provides unbiased estimate for the genetic effect, and discuss how to develop a family-based association test using IPTW for family-based studies. We apply the developed methods to systolic blood pressure data in Framingham Heart Study, where some subjects took antihypertensive treatment during the course of study.