A nonparametric approach to confidence intervals for concordance index and difference between correlated indices

Concordance refers to the probability that subjects with high values on one variable also have high values on another variable. This index has wide application in practice, as a measure of effect size in group-comparison studies, an index of accuracy in diagnostic studies, and a discrimination index for prediction models. Herein, we provide a unified framework for statistical inference involving concordance indices for standard variables of binary, ordinal, and continuous types. In particular, we develop confidence interval procedures for a single concordance index and differences between two correlated indices. Simulation results show that procedures based on logit-transformation for a single index and Fisher's z-transformation for a difference between indices perform very well in terms of coverage and tail errors even when the sample size is as small as 30, unless the concordance is high and the standard is a binary variable for which at least 50 subjects are needed. We illustrate the procedures for a variety of standard variables with previously published data. Illustrative SAS code is provided.

Automated summary

Concordance refers to the probability that high values on one variable are also observed on another variable. This index has various applications in practice, such as measuring effect size, evaluating accuracy, and assessing discrimination in prediction models. In this paper, the authors propose a unified framework for statistical inference of concordance indices for binary, ordinal, and continuous variables. Confidence interval procedures are developed for single and correlated indices, and simulation results demonstrate the effectiveness of the proposed methods.