Primary Submission Category: Difference in Differences, Synthetic Control, Methods for Panel and Longitudinal Data
Estimating effects of longitudinal modified treatment policies (LMTPs) on rates of change in health outcomes with repeated measures data
Authors: Anja Shahu, Daniel Malinsky,
Presenting Author: Daniel Malinsky*
Longitudinal modified treatment policies (LMTPs) quantify the effects of interventions that depend on the natural value of exposure, generalizing “stochastic” and “shift” interventions as well as other policy-relevant quantities. The current LMTP estimation approach yields effects on outcomes measured at the end of a study; however, repeated measures data often contains time-varying outcomes measured at each visit and interest may lie in estimating effects on the rate of change in these outcomes over time. For example, one may wish to quantify the effect of an LMTP on the rate of progression of a disease. We extend the LMTP approach to estimate the effect on change in a time-varying outcome over time and propose a hypothesis testing framework to formally test whether there is a difference in change in the outcome over time under an LMTP versus the natural outcome trajectory (or versus a different LMTP). Repeated measures data also frequently has unique data complications that must be considered. One such complication is that of irregular visit times, where the visit timing varies among individuals from some pre-specified time. We propose an extension to our work that permits effect estimation and hypothesis testing for an LMTP in a setting with irregular visit times. We present results from a simulation study which shows that ignoring irregular visit times may lead to bias, and we illustrate our hypothesis testing framework in both regular and irregular visit time settings.