A multiple robust propensity score method for longitudinal analysis with intermittent missing data.

Chen, Chixiang; Shen, Biyi; Liu, Aiyi; Wu, Rongling; Wang, Ming

Chen, Chixiang; Shen, Biyi; Liu, Aiyi; Wu, Rongling; Wang, Ming.

Afiliação

Chen C; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.
Shen B; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.
Liu A; Biostatistics and Bioinformatics Branch, National Institute of Child Health and Human Development, NIH, Bethesda, Maryland.
Wu R; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.
Wang M; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.

Biometrics ; 77(2): 519-532, 2021 06.

Article em En | MEDLINE | ID: mdl-32662124

Assuntos

Modelos Estatísticos; Projetos de Pesquisa; Interpretação Estatística de Dados; Funções Verossimilhança; Pontuação de Propensão

Palavras-chave

empirical likelihood; missing at random; propensity scores; semiparametric models; variable selection

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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 Idioma: En Revista: Biometrics Ano de publicação: 2021 Tipo de documento: Article

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