Calibration plots for risk prediction models in the presence of competing risks
Calibration plots for risk prediction models in the presence of competing risks
复制标题
DOI：
10.1002/sim.6152
复制
发表时间：
2014-08-15
影响因子：
2
通讯作者：
Kattan, Michael W.
中科院分区：
文献类型：
--
作者：
Gerds, Thomas A.;Andersen, Per K.;Kattan, Michael W.
如果100例患者中有17例预期风险为17%，则17%的预测风险可被称为可靠。统计模型可以预测事件的绝对风险，例如在存在竞争风险（例如由于其他原因导致的死亡）的情况下心血管死亡。对于个性化医疗和患者咨询，有必要检查模型是否在为所有受试者提供可靠预测的意义上进行了校准。当目标是显示或测试风险预测模型是否经过良好校准时，经常会遇到三个实际问题。第一是缺乏独立的验证数据，第二是右删失，第三是当风险标度连续时，估计问题与密度估计一样困难。为了处理这些问题，我们建议估计校准曲线的竞争风险模型的基础上刀切伪值，结合最近的邻域平滑和交叉验证方法来处理所有三个问题。版权所有（C）2014约翰威利父子有限公司
A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright (C) 2014 John Wiley & Sons, Ltd.