Citation
Coulombe, J. & Yang, S. (2024). Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations. Biometrics. vol. 80 (3) , PMCID: 39011739Abstract
Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents. © The Author(s) 2024.URL
https://doi.org/10.1093/biomtc/ujae065Keyword(s)
average treatment effectNotes
Export Date: 14 August 2024; Cited By: 0; Correspondence Address: J. Coulombe; Department of Mathematics and Statistics, Université de Montréal, Montreal, 2920 Chemin de la Tour, H3T 1J4, Canada; email: janie.coulombe@umontreal.ca; CODEN: BIOMAReference Type
Journal ArticleJournal Title
BiometricsAuthor(s)
Coulombe, J.Yang, S.