A program for computing the prediction probability and the related receiver operating characteristic graph.

Prediction probability (P(K)) and the area under the receiver operating characteristic curve (AUC) are statistical measures to assess the performance of anesthetic depth indicators, to more precisely quantify the correlation between observed anesthetic depth and corresponding values of a monitor or indicator. In contrast to many other statistical tests, they offer several advantages. First, P(K) and AUC are independent from scale units and assumptions on underlying distributions. Second, the calculation can be performed without any knowledge about particular indicator threshold values, which makes the test more independent from specific test data. Third, recent approaches using resampling methods allow a reliable comparison of P(K) or AUC of different indicators of anesthetic depth. Furthermore, both tests allow simple interpretation, whereby results between 0 and 1 are related to the probability, how good an indicator separates the observed levels of anesthesia. For these reasons, P(K) and AUC have become popular in medical decision making. P(K) is intended for polytomous patient states (i.e., >2 anesthetic levels) and can be considered as a generalization of the AUC, which was basically introduced to assess a predictor of dichotomous classes (e.g., consciousness and unconsciousness in anesthesia). Dichotomous paradigms provide equal values of P(K) and AUC test statistics. In the present investigation, we introduce a user-friendly computer program for computing P(K) and estimating reliable bootstrap confidence intervals. It is designed for multiple comparisons of the performance of depth of anesthesia indicators. Additionally, for dichotomous classes, the program plots the receiver operating characteristic graph completing information obtained from P(K) or AUC, respectively. In clinical investigations, both measures are applied for indicator assessment, where ambiguous usage and interpretation may be a consequence. Therefore, a summary of the concepts of P(K) and AUC including brief and easily understandable proof of their equality is presented in the text. The exposure introduces readers to the algorithms of the provided computer program and is intended to make standardized performance tests of depth of anesthesia indicators available to medical researchers.