A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data

BACKGROUND: Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expen...

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Detalles Bibliográficos
Autores principales: Lin, Jung-Yi Joyce, Hu, Liangyuan, Huang, Chuyue, Jiayi, Ji, Lawrence, Steven, Govindarajulu, Usha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066834/
https://www.ncbi.nlm.nih.gov/pubmed/35508974
http://dx.doi.org/10.1186/s12874-022-01608-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066834/
https://www.ncbi.nlm.nih.gov/pubmed/35508974
http://dx.doi.org/10.1186/s12874-022-01608-7

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