An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes.

Geroldinger, Angelika; Blagus, Rok; Ogden, Helen; Heinze, Georg

Geroldinger, Angelika; Blagus, Rok; Ogden, Helen; Heinze, Georg.

Afiliação

Geroldinger A; Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent System, Section for Clinical Biometrics, Spitalgasse 23, A-1090, Vienna, Austria.
Blagus R; University of Ljubljana, Institute of Biostatistics and Medical Informatics, Ljubljana, Slovenia.
Ogden H; University of Southampton, School of Mathematical Sciences, Southampton, UK.
Heinze G; Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent System, Section for Clinical Biometrics, Spitalgasse 23, A-1090, Vienna, Austria. georg.heinze@meduniwien.ac.at.

BMC Med Res Methodol ; 22(1): 168, 2022 06 09.

Article em En | MEDLINE | ID: mdl-35681120

Assuntos

Modelos Estatísticos; Viés; Simulação por Computador; Humanos; Funções Verossimilhança; Modelos Logísticos

Palavras-chave

Clustered data; Firth's logistic regression; Generalized estimating equations; Logistic regression; Non-convergence; Separation

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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