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dc.contributor.authorSiddique, Arman Alam
dc.contributor.authorSchnitzer, Mireille
dc.contributor.authorBahamyirou, Asma
dc.contributor.authorGuanbo, Wang
dc.contributor.authorHoltz, Timothy H.
dc.contributor.authorMigliori, Giovanni B
dc.subjectCausal inferencefr
dc.subjectConcurrent medicationsfr
dc.subjectGeneralized propensity scorefr
dc.subjectMachine learningfr
dc.subjectMultidrug-resistant tuberculosisfr
dc.subjectTargeted maximum likelihood estimationfr
dc.titleCausal inference with multiple concurrent medications: a comparison of methods and an application in multidrug-resistant tuberculosisfr
dc.contributor.affiliationUniversité de Montréal. Faculté de pharmaciefr
dc.contributor.affiliationUniversité de Montréal. École de santé publique. Département de médecine sociale et préventivefr
dcterms.abstractThis paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary
dcterms.descriptionSimulation study code available at
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleStatistical methods in medical research

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