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arXiv · 2204.03447

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

Camille Nevoret·Sandrine Katsahian·Agathe Guilloux
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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.

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Camille Nevoret, Sandrine Katsahian, Agathe Guilloux. 2022-04-07. Debiasing the estimate of treatment effect on the treated with time-varying counfounders. https://arxiv.org/abs/2204.03447

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