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A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.
Wu, Jing; Chen, Ming-Hui; Schifano, Elizabeth D; Ibrahim, Joseph G; Fisher, Jeffrey D.
Afiliación
  • Wu J; Department of Computer Science and Statistics, University of Rhode Island, Kingston, Rhode Island.
  • Chen MH; Department of Statistics, University of Connecticut, Storrs, Connecticut.
  • Schifano ED; Department of Statistics, University of Connecticut, Storrs, Connecticut.
  • Ibrahim JG; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Fisher JD; Department of Psychology and Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, Connecticut.
Stat Med ; 38(30): 5565-5586, 2019 12 30.
Article en En | MEDLINE | ID: mdl-31691322
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infecciones por VIH / Modelos Estadísticos Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male Idioma: En Revista: Stat Med Año: 2019 Tipo del documento: Article
Texto completo
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infecciones por VIH / Modelos Estadísticos Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male Idioma: En Revista: Stat Med Año: 2019 Tipo del documento: Article