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

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