Population-level and individual-level explainers for propensity score matching in observational studies

PRECIS: The exclusion of unmatched observations in propensity score matching has implications for the generalizability of causal effects. Machine learning methods can help to identify how the study population differs from the unmatched subpopulation. BACKGROUND: There has been widespread use of prop...

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Detalles Bibliográficos
Autores principales: Ghosh, Debashis, Amini, Arya, Jones, Bernard L., Karam, Sana D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630947/
https://www.ncbi.nlm.nih.gov/pubmed/36338745
http://dx.doi.org/10.3389/fonc.2022.958907

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630947/
https://www.ncbi.nlm.nih.gov/pubmed/36338745
http://dx.doi.org/10.3389/fonc.2022.958907

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