Assessing and comparing the performance of correlated predictive scores are of current interest in precision medicine. Given the limitations of available theoretical approaches for assessing and comparing the predictive accuracy, numerical methods are highly desired which, however, have not been systematically developed due to technical challenges. The main challenges include the lack of a general strategy on effectively simulating many kinds of correlated predictive scores each with some given level of predictive accuracy in either concordance index or the area under a receiver operating characteristic curve area under the curves (AUC). To fill in this important knowledge gap, this paper is to provide a general copula‐based numeric framework for assessing and comparing predictive performance of correlated predictive or risk scores. The new algorithms are designed to effectively simulate correlated predictive scores with given levels of predictive accuracy as measured in terms of concordance indices or time‐dependent AUC for predicting survival outcomes. The copula‐based numerical strategy is convenient for numerically evaluating and comparing multiple measures of predictive accuracy of correlated risk scores and for investigating finite‐sample properties of test statistics and confidence intervals as well as assessing for optimism of given performance measures using cross‐validation or bootstrap.

中文翻译：

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通过基于 copula 的方法评估预测分数性能的数值策略。

评估和比较相关预测分数的性能是目前精准医学的兴趣所在。鉴于用于评估和比较预测精度的可用理论方法的局限性，非常需要数值方法，但是由于技术挑战尚未系统地开发。主要挑战包括缺乏有效模拟多种相关预测分数的通用策略，每个分数在一致性指数或接受者操作特征曲线下的面积（AUC）中具有一定的预测准确度。为了填补这一重要的知识空白，本文提供了一个基于 copula 的通用数字框架，用于评估和比较相关预测或风险评分的预测性能。新算法旨在有效模拟具有给定预测准确度水平的相关预测分数，如根据一致性指数或时间相关的 AUC 衡量，以预测生存结果。基于 copula 的数值策略便于数值评估和比较相关风险评分的预测准确性的多种度量，以及调查测试统计和置信区间的有限样本属性，以及使用交叉验证或引导程序。