BiMM tree: A decision tree 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.

Commun Stat Simul Comput ; 49(4): 1004-1023, 2020.

Article en En | MEDLINE | ID: mdl-32377032

Palabras clave

classification and regression tree; clustered data; decision tree; longitudinal data; mixed effects

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Año: 2020 Tipo del documento: Article

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