A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial

Personalizing treatment recommendations or guidelines requires evidence about the heterogeneity of treatment effects (HTE). Machine-learning (ML) approaches can explore HTE by considering many covariates, including complex interactions between them. Causal ML approaches can avoid overfitting, which...

Descripción completa

Detalles Bibliográficos
Autores principales: Sadique, Zia, Grieve, Richard, Diaz-Ordaz, Karla, Mouncey, Paul, Lamontagne, Francois, O’Neill, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Original Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459357/
https://www.ncbi.nlm.nih.gov/pubmed/35607982
http://dx.doi.org/10.1177/0272989X221100717

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459357/
https://www.ncbi.nlm.nih.gov/pubmed/35607982
http://dx.doi.org/10.1177/0272989X221100717

Ejemplares similares