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.
Artigo
em Inglês
| 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:
DisponíveL
Índice:
LILACS (Américas)
Assunto principal:
Causalidade
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Inglês
Revista:
Invest. educ. enferm
Assunto da revista:
Educação
/
Enfermagem
Ano de publicação:
2019
Tipo de documento:
Artigo
País de afiliação:
Colômbia
Instituição/País de afiliação:
Universidad de Antioquia/CO
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