The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models

Abstract

Many measures of prediction accuracy have been developed. However, the most popular ones in typical medical outcome prediction settings require additional investigation of calibration. We show how rescaling the Brier score produces a measure that combines discrimination and calibration in one value and improves interpretability by adjusting for a benchmark model. We have called this measure the index of prediction accuracy (IPA). The IPA permits a common interpretation across binary, time to event, and competing risk outcomes. We illustrate this measure using example datasets. The IPA is simple to compute, and example code is provided. The values of the IPA appear very interpretable. IPA should be a prominent measure reported in studies of medical prediction model performance. However, IPA is only a measure of average performance and, by default, does not measure the utility of a medical decision.