Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

Kim, Sungduk; Caporaso, Neil E; Gu, Fangyi; Klerman, Elizabeth B; Albert, Paul S

Kim, Sungduk; Caporaso, Neil E; Gu, Fangyi; Klerman, Elizabeth B; Albert, Paul S.

Afiliação

Kim S; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Caporaso NE; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Gu F; Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
Klerman EB; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Albert PS; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.

Stat Med ; 42(18): 3302-3315, 2023 08 15.

Article em En | MEDLINE | ID: mdl-37232457

Assuntos

Ritmo Circadiano; Humanos; Teorema de Bayes

Palavras-chave

Bayesian; circadian rhythms; diurnal rhythms; latent class model; longitudinal data; metabolite data; random effects

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ritmo Circadiano Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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