For competing risks data, it is often important to predict a patient's outcome status at a clinically meaningful time point after incorporating the informative censoring due to competing risks. This can be done by adopting a regression model that relates the cumulative incidence probabilities to a set of covariates. To assess the performance of the resulting prediction tool, we propose an estimator of the polytomous discrimination index applicable to competing risks data, which can quantify a prognostic model's ability to discriminate among subjects from different outcome groups. The proposed estimator allows the prediction model to be subject to model misspecification and enjoys desirable asymptotic properties. We also develop an efficient computation algorithm that features a computational complexity of $O(n\log n)$. A perturbation resampling scheme is developed to achieve consistent variance estimation. Numerical results suggest that the estimator performs well under realistic sample sizes. We apply the proposed methods to a study of monoclonal gammopathy of undetermined significance.

中文翻译：

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使用多分判别指数评估竞争风险预测模型

对于竞争风险数据，在合并由于竞争风险导致的信息审查后，在临床有意义的时间点预测患者的结果状态通常很重要。这可以通过采用将累积发生概率与一组协变量相关联的回归模型来完成。为了评估生成的预测工具的性能，我们提出了适用于竞争风险数据的多分类歧视指数的估计量，它可以量化预后模型区分来自不同结果组的受试者的能力。所提出的估计器允许预测模型受到模型错误指定的影响，并享有理想的渐近特性。我们还开发了一种高效的计算算法，其计算复杂度为$○(n\mathrm{日志}n)$. 开发了一种扰动重采样方案以实现一致的方差估计。数值结果表明，估计器在实际样本量下表现良好。我们将所提出的方法应用于意义不明的单克隆丙种球蛋白病的研究。