Causal inference for observational studies with clustered survival outcomes subject to right-censoring

Music City Center 

Clustered time-to-event data are increasingly common in health research, particularly in observational studies such as with electronic health records or administrative data. For example, time-to-event data are often collected from individuals within shared environments such as hospitals, which may result in interdependence between observations. To quantify treatment effects from observational data we typically rely on causal inference approaches such as propensity score weighting to control for confounding. However, despite extensive literature on causal approaches for time-to-event data and clustered data individually, methods for incorporating both remain understudied. Motivated by this methodological gap, we extend existing causal survival estimators to accommodate clustered data and investigate their performance in a comprehensive simulation study with emphasis on varying clustering perspectives and robustness to misspecified propensity score and censoring score models. We lastly provide an accessible R tutorial to demonstrate how to implement our estimators with a real-world clinical example as to provide practical guidance for applied researchers interested in clustered causal survival analysis.

Biostatistics

Causal Inference

Survival Analysis

Longitudinal/Correlated Data

Random Effects and Mixed Models

Electronic Health Records 

Biometrics Section