[Dichotomization of continuous variables in logistic regression models]. 1996

F Cumsille, and S I Bangdiwala
Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Santiago de Chile.

It is highly to observe in the biomedical literature, that the continuous variables such as systolic blood pressure and cholesterol, are dichotomized and used in such manner in a given statistical analysis. The consequences of such transformation can be very varied. In this article, how the dichotomization of a continuous exposure variable affects the quality of the prediction of a response using the logistic regression is examined. One can conclude that, in almost all the studied stimulation, the percentages of misclassification can increase drastically, reaching over there times the probability of misclassification of the ones using the original variable. Therefore, we recommend to avoid dichotomization.

UI MeSH Term Description Entries
D016015 Logistic Models Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor. Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic

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