Assessing Discriminatory Performance of a Binary Logistic Regression Model

Abstract

The evaluation of fitted binary logistic regression model is very important in assessing the appropriateness of a model for specific purposes. The study proposes to assess the discriminatory performance of a binary logistic regression model to correctly classify between the cases and non-cases. The discriminatory performance of binary logistic regression model is measured using two approaches. The first approach is the use of fitted binary logistic regression model to correctly predict the subjects that are cases and non-cases, with the help of the parameters sensitivity and specificity. The alternative approach is based on receiver operating characteristic (ROC) curve for the fitted binary logistic regression model and then determining the area under the curve (AUC) as a measure of discriminatory performance. The value of sensitivity is observed to be greater than the value of 1- specificity, which signifies suitable discrimination for the mentioned cut point. The area under the curve indicates that there is evidence of reasonable discrimination reported by the fitted model.