BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes.

Speiser, Jaime Lynn; Wolf, Bethany J; Chung, Dongjun; Karvellas, Constantine J; Koch, David G; Durkalski, Valerie L

Speiser, Jaime Lynn; Wolf, Bethany J; Chung, Dongjun; Karvellas, Constantine J; Koch, David G; Durkalski, Valerie L.

Afiliación

Speiser JL; Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC.
Wolf BJ; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.
Chung D; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.
Karvellas CJ; Divisions of Hepatology and Critical Care Medicine, University of Alberta, Edmonton, Canada.
Koch DG; Division of Gastroenterology and Hepatology, Department of Medicine, Medical University of South Carolina, Charleston, SC.
Durkalski VL; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.

Chemometr Intell Lab Syst ; 185: 122-134, 2019 Feb 15.

Article en En | MEDLINE | ID: mdl-31656362

Palabras clave

clustered data; longitudinal data; mixed effects; random forest

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chemometr Intell Lab Syst Año: 2019 Tipo del documento: Article País de afiliación: Nueva Caledonia

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