Investigating the appropriateness of different concordance measures in a time-to-event setting

Prediction models that assess a patient's risk of an event are used to inform treatment options and confirm screening tests. The concordance (c) statistic is one measure to validate the accuracy of these models, but has many extensions when applied to censored data. The purpose was to determine which c-statistic is most accurate at different rates of censoring.

A simulation study was conducted for n = 750, and censoring rates of 20%, 50%, and 80%. The mean of three different concordance definitions were compared as well as the mean of three different c-statistics, including one, parametric c-statistic for exponentially distributed data, developed by the authors. The SE was also calculated but was of secondary interest.

The c-statistic developed by the authors yielded the a mean closest to the gold standard concordance measure when censoring is present in data, even when the exponentially distributed parametric assumptions do not hold. Similar results were found for SE.

The c-statistic developed by the authors appears to be the most robust to censored data. Thus, it is recommended to use this c-statistic to validate prediction models applied to censored data. This will improve the reliability and comparability across future time-to-event studies.