A doubly robust method to handle missing multilevel outcome data with application to the China Health and Nutrition Survey.

Butera, Nicole M; Zeng, Donglin; Green Howard, Annie; Gordon-Larsen, Penny; Cai, Jianwen

Butera, Nicole M; Zeng, Donglin; Green Howard, Annie; Gordon-Larsen, Penny; Cai, Jianwen.

Afiliação

Butera NM; The Biostatistics Center, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Rockville, Maryland, USA.
Zeng D; Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Green Howard A; Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Gordon-Larsen P; Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Cai J; Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Stat Med ; 41(4): 769-785, 2022 02 20.

Article em En | MEDLINE | ID: mdl-34786739

Assuntos

Modelos Estatísticos; Projetos de Pesquisa; Viés; Simulação por Computador; Humanos; Estudos Longitudinais; Inquéritos Nutricionais

Palavras-chave

clustering; doubly robust; hierarchical modeling; longitudinal; missing data

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Modelos Estatísticos Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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