Debiasing the estimate of treatment effect on the treated with time-varying counfounders

Abstract

With the increased availability of large health databases comes the opportunity of evaluating treatment effect on new data sources.Through these databases time-dependent outcomes can be analysed as events that can be measured using counting processes. Estimating average treatment effect on the treated (ATT) requires modelling of time-varying covariate and time-dependent treatment and outcome. Gran et al. proposed an easy-to-implement method based on additive intensity regression to estimate ATT. We introduce a debiased estimate of the ATT based on a generalization of the Gran's model for a potentially repeated outcome and in the presence of multiple time-dependent covariates and baseline covariates. Simulation analyses show that our corrected estimator outperforms Gran's uncorrected estimator. Our method is applied to intensive care real-life data from MIMIC-III databases to estimate vasoppressors effect on patients with sepsis.