Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes

INTRODUCTION: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but iden...

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Detalles Bibliográficos
Autores principales: Wolf, Bethany J., Jiang, Yunyun, Wilson, Sylvia H., Oates, Jim C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057419/
https://www.ncbi.nlm.nih.gov/pubmed/33948279
http://dx.doi.org/10.1017/cts.2020.556

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057419/
https://www.ncbi.nlm.nih.gov/pubmed/33948279
http://dx.doi.org/10.1017/cts.2020.556

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