We propose an inverse probability of censoring weighted (IPCW) bagging (bootstrap aggregation) pre‐processing that enables the application of any machine learning procedure for classification to be used to predict the cause‐specific cumulative incidence, properly accounting for right‐censored observations and competing risks. We consider the IPCW area under the time‐dependent ROC curve (IPCW‐AUC) as a performance evaluation metric. We also suggest a procedure to optimally stack predictions from any set of IPCW bagged methods. We illustrate our proposed method in the Swedish InfCareHIV register by predicting individuals for whom treatment will not maintain an undetectable viral load for at least 2 years following initial suppression. The R package stackBagg that implements our proposed method is available on Github.

中文翻译：

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审查加权袋装的堆叠逆概率：以InfCareHIV登记册为例

我们提出了一种对加权（IPCW）套袋（bootstrap聚合）进行预处理的逆概率，该预处理使使用任何机器学习分类程序可以预测特定原因的累积发生率，适当考虑到右删失的观测值和竞争风险。我们将基于时间的ROC曲线（IPCW-AUC）下的IPCW面积视为性能评估指标。我们还建议了一种从IPCW套袋方法的任何集合中最佳地堆叠预测的过程。我们通过预测在最初抑制后至少2年内治疗不会维持无法检测到的病毒载量的个体，从而说明了我们在瑞典InfCareHIV注册中提出的方法。R包的堆栈 可以在Github上实现实现我们提出的方法的工具。