Treatment persistence drives estimator performance in longitudinal causal inference based on observational data: A simulation study
TL;DR
Results show that treatment persistence is the main driver of estimator behaviour, and emphasise that estimator performance should be interpreted jointly with the target intervention and the treatment process generating the observed data.
Abstract
Longitudinal clinical data are increasingly available, offering opportunities to study treatment effects over time but also raising challenges related to time-varying confounding and evolving treatment decisions. We investigate how longitudinal treatment dynamics affect causal effect estimation when baseline and longitudinal methods target different causal estimands. Using a structural causal model (SCM), we simulate data with time-varying confounding, binary treatment, and an absorbing binary outcome under 9 scenarios combining functional complexity and treatment persistence. We compare three baseline and two longitudinal estimators against Monte Carlo ground-truth risk ratios (RRs) under sustained treatment regimes. Results show that treatment persistence is the main driver of estimator behaviour. High persistence reduces the discrepancy between baseline and sustained-regime estimands, making baseline estimators closer to the sustained-regime ground truth (average relative deviation of baseline IPTW/TMLE decreasing from 61% under low persistence to 10% under high persistence), whereas low persistence induces practical positivity challenges and increases the variability of longitudinal estimators, with empirical 95% interval widths increasing from 0.22 to 0.46 for longitudinal IPTW and from 0.20 to 0.36 for LTMLE when moving from high to low persistence. These findings emphasise that estimator performance should be interpreted jointly with the target intervention and the treatment process generating the observed data.