Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE: In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as...

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Detalles Bibliográficos
Autores principales: Zhong, Yongqi, Brooks, Maria M., Kennedy, Edward H., Bodnar, Lisa M., Naimi, Ashley I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8908068/
https://www.ncbi.nlm.nih.gov/pubmed/35262718
http://dx.doi.org/10.1001/jamanetworkopen.2021.43414

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8908068/
https://www.ncbi.nlm.nih.gov/pubmed/35262718
http://dx.doi.org/10.1001/jamanetworkopen.2021.43414

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