  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.
