Dynamic Propensity Trajectory Modeling and Matching with Time-Dependent Covariates for Causal Inference

Music City Center 

In observational studies, propensity score (PS)-based causal inference techniques are commonly utilized to address selection bias in treatment assignment. Most existing PS research focuses on time-invariant treatments within a cross-sectional design. Limited attention has been given to PS processes in a longitudinal context involving survival endpoints, and even less work exists regarding time-varying treatments. Note that time-varying propensity score matching methods, as proposed by Lu (2005), have addressed time-dependent treatment receipt but have primarily been limited to continuous outcome measures, with only modest extensions. These methods consider pretreatment characteristics at a specific time point t without fully leveraging historical hazard information preceding time t. To bridge this gap, we introduce the dynamic propensity trajectory (DPT) framework and DPT-based matching (DPTM) techniques. These approaches achieve covariate balance across the entire study period, encompassing both time-invariant and time-varying covariates leading up to treatment initiation. In the primary analysis after matching, we quantify the causal treatment effects for time-to-event outcomes following treatment initiation. We apply the proposed methods to the Chronic Renal Insufficiency Cohort (CRIC) study to investigate the effects of antihypertensive medications in reducing the risk of cardiovascular disease among patients with chronic kidney disease. Additionally, we evaluate these methods in simulation studies, where our approaches outperform existing ones and result in the smallest bias.

Causal treatment Effect

Cox Proportional Hazard model

Observational Study

Propensity Score

Time-dependent Confounders