Time-Varying Treatment Effect Estimation With Irregular Patient Visit Data

Music City Center 

Estimating time-varying treatment effects is essential for guiding clinical decisions, particularly in chronic disease management. However, applying existing causal inference methods to observational data, such as electronic health records (EHR), is challenging due to irregular patient visit patterns. A common approach uses multiple imputation to fill in missing data before applying causal methods, but this increases modeling complexity and may be inefficient. We proposed a sequential analysis using a Bayesian additive regression trees (BART) model that directly accommodates irregular visit patterns, allowing the visit mechanism to depend on unobserved data. Our method also handles treatment heterogeneity, enabling more accurate effect estimation for individualized treatment decisions. Through simulation studies, we show that our approach significantly improves estimation compared to standard two-step practices relying on multiple imputation. We illustrate its use with EHR data from a juvenile idiopathic arthritis study.

Time-varying treatment effects

Irregular longitudinal data

Multiple imputation

Bayesian additive regression trees 

Biometrics Section