Bayesian nonparametric generative models for causal inference with missing at random covariates.

Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J

Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J.

Afiliación

Roy J; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Lum KJ; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Zeldow B; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Dworkin JD; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Re VL; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Daniels MJ; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.

Biometrics ; 74(4): 1193-1202, 2018 12.

Article en En | MEDLINE | ID: mdl-29579341

Asunto(s)

Teorema de Bayes; Biometría/métodos; Causalidad; Simulación por Computador; Algoritmos; Estudios de Cohortes; Coinfección/virología; Factores de Confusión Epidemiológicos; Infecciones por VIH/virología; Hepatitis C/virología; Humanos; Modelos Estadísticos; Estudios Observacionales como Asunto

Palabras clave

Bayesian modeling; Causal effect; Cluster; Enriched Dirichlet process mixture model; Missing data; Observational studies

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Simulación por Computador / Causalidad / Teorema de Bayes / Biometría Tipo de estudio: Clinical_trials / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Biometrics Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

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