A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.

Hu, Liangyuan; Zou, Jungang; Gu, Chenyang; Ji, Jiayi; Lopez, Michael; Kale, Minal

Hu, Liangyuan; Zou, Jungang; Gu, Chenyang; Ji, Jiayi; Lopez, Michael; Kale, Minal.

Afiliación

Hu L; Department of Biostatistics and Epidemiology, Rutgers University.
Zou J; Department of Biostatistics, Columbia University.
Gu C; Analysis Group, Inc.
Ji J; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai.
Lopez M; Department of Mathematics, Skidmore College.
Kale M; Department of Medicine, Icahn School of Medicine at Mount Sinai.

Ann Appl Stat ; 16(2): 1014-1037, 2022 Jun.

Article en En | MEDLINE | ID: mdl-36644682

Palabras clave

Bayesian inference; Causal inference; ignorability assumption; nested multiple imputation; observational data

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies Idioma: En Revista: Ann Appl Stat Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

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