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A sequential conditional mean model for assessing total effects of exposure in longitudinal data / 中华流行病学杂志
Chinese Journal of Epidemiology ; (12): 111-114, 2020.
Article en Zh | WPRIM | ID: wpr-798891
Biblioteca responsable: WPRO
ABSTRACT
In prospective cohort study, multi follow up is often necessary for study subjects, and the observed values are correlated with each other, usually resulting in time-dependent confounding. In this case, the data generally do not meet the application conditions of traditional multivariate regression analysis. Sequential conditional mean model (SCMM) is a new approach that can deal with time-dependent confounding. This paper mainly summarizes the basic theory, steps and characteristics of SCMM.
Palabras clave
Texto completo: 1 Índice: WPRIM Tipo de estudio: Observational_studies / Prognostic_studies Idioma: Zh Revista: Chinese Journal of Epidemiology Año: 2020 Tipo del documento: Article
Texto completo: 1 Índice: WPRIM Tipo de estudio: Observational_studies / Prognostic_studies Idioma: Zh Revista: Chinese Journal of Epidemiology Año: 2020 Tipo del documento: Article