Empirical use of causal inference methods to evaluate survival differences in a real-world registry vs those found in randomized clinical trials.

Lee, Hui-Jie; Wong, John B; Jia, Beilin; Qi, Xinyue; DeLong, Elizabeth R

Lee, Hui-Jie; Wong, John B; Jia, Beilin; Qi, Xinyue; DeLong, Elizabeth R.

Afiliación

Lee HJ; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Wong JB; Tufts Medical Center, Division of Clinical Decision Making, Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, USA.
Jia B; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Qi X; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
DeLong ER; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Stat Med ; 39(22): 3003-3021, 2020 09 30.

Article en En | MEDLINE | ID: mdl-32643219

Asunto(s)

Enfermedad de la Arteria Coronaria; Intervención Coronaria Percutánea; Puente de Arteria Coronaria; Humanos; Ensayos Clínicos Controlados Aleatorios como Asunto; Sistema de Registros; Resultado del Tratamiento

Palabras clave

inverse probability of treatment weighting; multiple imputation; observational studies; optimal full matching; propensity score; trial eligibility

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria / Intervención Coronaria Percutánea Tipo de estudio: Clinical_trials / Observational_studies Límite: Humans Idioma: En Revista: Stat Med Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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