Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity

ABSTRACT With the growing availability of large healthcare databases for clinical science, mitigating unmeasured confounding has emerged as a major issue in pharmacoepidemiologic studies. Extensions of causal inference methods to high‐dimensional settings could help address this problem, but studies...

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Κύριοι συγγραφείς:	Chi‐Hong Duong, Sylvie Escolano, Romain Demailly, Anne Thiebaut, Jonathan Cottenet, Catherine Quantin, Pascale Tubert‐Bitter, Ismaïl Ahmed
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	Wiley 2025-11-01
Σειρά:	Clinical and Translational Science
Θέματα:	g‐computation machine learning preterm delivery propensity score targeted learning unmeasured confounders
Διαθέσιμο Online:	https://doi.org/10.1111/cts.70394
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