Use of Causal Diagrams for Nursing Research: a Tool for Application in Epidemiological Studies
Invest. educ. enferm
;
37(1): [E01], Febrero 2019. Figure 1. Direct acyclic graph to represent the relationship between metabolic syndrome and global longitudinal strain.
Artículo
en Inglés
| LILACS, BDENF, COLNAL
| ID: biblio-981688
ABSTRACT
Many epidemiological studies seek to assess the effect of one or several exposures on one or more outcomes. However, to quantify the causal inference produced, statistical techniques are commonly used that contrast the association among the variables of interest, not precisely of causal effect.(1) In fact, although these measures may not have a causal interpretation, the results are often adjusted for all potential confounding factors. (2,3) Some contemporary epidemiologists developed new methodological tools for causal inference, like the theory or contra-factual model(4) and representation of causal effects through the Directed Acyclic Graph (DAG).(5) The DAG, a fusion of the probability theory with trajectory diagrams, is quite useful to visually deduct the statistical associations implied by the causal relations among the study variables.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Causalidad
Tipo de estudio:
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
Invest. educ. enferm
Asunto de la revista:
Educación
/
Enfemeria
Año:
2019
Tipo del documento:
Artículo
País de afiliación:
Colombia
Institución/País de afiliación:
Universidad de Antioquia/CO
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