A new algorithm for solving binary discrimination in conditional logistic regression, with two choices of strata
When conditional logistic regression is based on the exact conditional distribution for inference, the intercept is eliminated. This becomes a problem when the predicted probability is a key issue for binary discrimination. This report details a new algorithm for risk score instead of predicted probability for stratified data in binary discrimination. From the statistical point of view, data partition will reduce the variation of data. Comparing the data-inherent strata and strata generated from the Classification and Regression Tree (CART), the strata generated from CART had greater variation reduction than did the data-inherent strata. Finally, the conditional logistic regression algorithm, used for discrimination when modeling fetal biometric data, resulted in cost savings and computer time savings benefits.
binary discrimination strata risk score cart fetal biometric data conditional logistic model roc