16: Estimating effects of LMTPs on rates of change in health outcomes with repeated measures data

Music City Center 

Longitudinal modified treatment policies (LMTPs) quantify the effects of interventions that depend on the natural value of exposure, generalizing policy-relevant quantities, such as "stochastic" and "shift" interventions. 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 the outcome trajectory under an LMTP differs from the natural outcome trajectory. Repeated measures data also frequently has unique data complications that must be considered, such as 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.

Causal inference

Longitudinal data

Modified treatment policies

Rates of change

Nonparametric 

Section on Statistics in Epidemiology