Logistic Regression
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Abstract
Like contingency table analyses and 2 tests, logistic regression allows the analysis of dichotomous or binary outcomes with 2 mutually exclusive levels.1 However, logistic regression permits the use of continuous or categorical predictors and provides the ability to adjust for multiple predictors. This makes logistic regression especially useful for analysis of observational data when adjustment is needed to reduce the potential bias resulting from differences in the groups being compared.2 Use of standard linear regression for a 2-level outcome can produce very unsatisfactory results. Predicted values for some covariate values are likely to be either above the upper level (usually 1) or below the lower level of the outcome (usually 0). In addition, the validity of linear regression depends on the variability of the outcome being the same for all values of the predictors. This assumption of constant variability does not match the behavior of a 2-level outcome. So, linear regression is not adequate for such data, and logistic regression has been developed to fill this gap. Some recent examples of use of logistic regression in Circulation include the assessment of gender as a predictor of operative mortality after coronary artery bypass grafting surgery,3 an evaluation of the relationship between the TaqlB genotype and risk of cardiovascular disease in a meta-analysis,4 and an examination of the relationship between lipoprotein abnormalities and the incidence of diabetes.5