A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases

PURPOSE: Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challeng...

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Detalles Bibliográficos
Autores principales: Benasseur, Imane, Talbot, Denis, Durand, Madeleine, Holbrook, Anne, Matteau, Alexis, Potter, Brian J., Renoux, Christel, Schnitzer, Mireille E., Tarride, Jean‐Éric, Guertin, Jason R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304306/
https://www.ncbi.nlm.nih.gov/pubmed/34953160
http://dx.doi.org/10.1002/pds.5403

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304306/
https://www.ncbi.nlm.nih.gov/pubmed/34953160
http://dx.doi.org/10.1002/pds.5403

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