时间相关受试者工作特征曲线及其在临床试验诊断分析中的应用

Time related receiver operation characteristic curves and its application in clinical trials and diagnostic analysis

利用R语言通过实例介绍生存模型中诊断指标的两种时间相关受试者工作特征[ROC（t）]曲线估计方法，即以NNE（nearest—neighbor estimator of bivariate distribution）估计法获得累积／动态的ROCC/D（t）曲线和以Cox估计法获得事件／动态的ROCI/D（t）曲线。分析显示利用两种估计法获得的ROC曲线下面积（AUC）值均随时间变化而波动，其中以NNE估计法得到的值波动较大，而用Cox法得到的曲线波动较小，但两种方法所得AUC均值相近。由此表明利用ROC（t）可对临床试验中诊断指标的诊断能力进行评价，有助于对诊断指标选择最佳的诊断时间，但使用中应注意选择相应的估计方法以获得更准确的评价。

By using R language to deal with practical problems, we introduce two methods of obtaining time related receiver operation characteristic [ROC（t）] curves from survival data： 1） nearest- neighbor estimator of bivariate distribution （NNE） estimation： to obtain cumulative/dynamic ROCC/D （t） curves; 2） Cox estimation： to obtain incident/dynamic ROCI/D （t） curves. The areas under the ROC（t） curves （AUC） obtained from the two methods fluctuate over time. The one obtained through NNE has bigger fluctuation than that obtained through Cox, while the mean of AUC of the two methods are similar. Time related ROC （t） can be effectively used to evaluate the diagnostic capacity of the marker in clinical trials, and help to select the best diagnostic time of the marker. According to the different scientific interests, researchers should select relevant methods for more accurate evaluation.