Polychotomization of continuous variables in regression models based on the overall C index

We used the overall discrimination index C, introduced by Harrel, as a measure of the predictive ability of an independent regressor variable and derived a method for polychotomization mathematically. Since the na？ve application of our method, like some existing methods, gives rise to positive bias, we developed a parametric method that minimizes this bias and assessed its performance by the use of Monte Carlo simulation.The overall C is closely related to the area under the ROC curve and the produced di(poly)chotomized variable's predictive performance is comparable to the original continuous variable. The simulation shows that the parametric method is essentially unbiased for both the estimates of performance and the cutoff points. Application of our method to the predictor variables of a previous study on rhabdomyolysis shows that it can be used to make probability profile tables that are applicable to the diagnosis or prognosis of individual patient status.We propose a polychotomization (including dichotomization) method for independent continuous variables in regression models based on the overall discrimination index C and clarified its meaning mathematically. To avoid positive bias in application, we have proposed and evaluated a parametric method. The proposed method for polychotomizing continuous regressor variables performed well and can be used to create probability profile tables.In modern diagnostic and descriptive prognostic research, regression models are often used to model an illness-related outcome based on a number of independent regressor variables, also referred to as diagnostic indicators or prognostic predictors [1]. Such regressor variables can be categorical or numerical. From the vantage point of applicability in a clinical setting, categorization (often dichotomization) of continuous independent variables can be useful. Obtaining a prediction at the bedside without computer is easier with a prediction table based on categorized variables t