Causal Inference
Comparative effects of generalized time-varying treatment strategies with repeatedly measured outcomes in EHR data Sean McGrath* Sean McGrath Jason P. Block Jessica G. Young
We consider the problem of estimating comparative effects of adhering to certain medication strategies on future weight gain based on electronic health records data. This problem presents several methodological challenges. First, this setting involves time-varying treatment strategies with treatment-confounder feedback. Second, the treatment strategies involve dynamic and non-deterministic elements, including grace periods. Third, the outcome is repeatedly measured (e.g., at each follow-up interval) with substantial missingness that follows a nonmonotonic pattern. Fourth, individuals may die during follow-up, in which case weight gain is undefined after death. In this talk, we describe approaches to estimate comparative effects that address the aforementioned challenges in our setting, which we refer to as time-smoothed inverse probability weighted (IPW) approaches. We conducted simulation studies that illustrate efficiency gains of the time-smoothed IPW approach over a more conventional IPW approach that does not leverage the repeated outcome measurements. We then applied the time-smoothed IPW approaches to estimate effects of adhering to antidepressant medication strategies on future weight gain.