Mathematics & Statistics, McMaster University, Hamilton, Canada.
Department of Oncology, McMaster University, Hamilton, Canada.
Pharm Stat. 2020 Nov;19(6):763-775. doi: 10.1002/pst.2029. Epub 2020 May 21.
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.
评估患者发生事件风险的预测模型用于为治疗方案提供参考并验证筛查试验。一致性（c）统计量是一种验证此类模型准确性的指标，但在应用于删失数据时，它有许多扩展。本研究旨在确定在不同删失率下哪种 c 统计量最准确。
对 n = 750 例进行模拟研究，删失率分别为 20%、50%和 80%。比较了三种不同一致性定义的平均值以及三种不同 c 统计量的平均值，包括作者开发的适用于指数分布数据的参数 c 统计量。同时还计算了标准误，但这是次要关注点。
当数据中存在删失时，作者开发的 c 统计量的平均值最接近金标准一致性测量值，即使指数分布的参数假设不成立。标准误也得到了类似的结果。
作者开发的 c 统计量似乎对删失数据最稳健。因此，建议在应用于删失数据的预测模型验证中使用该 c 统计量。这将提高未来生存时间研究的可靠性和可比性。
