Several methods to assess improvement in risk prediction models: Extension to survival analysis
Several methods to assess improvement in risk prediction models: Extension to survival analysis
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DOI：
10.1002/sim.4026
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发表时间：
2011-01-15
影响因子：
2
通讯作者：
Cui, Gang
中科院分区：
文献类型：
--
作者：
Chambless, Lloyd E.;Cummiskey, Christopher P.;Cui, Gang
风险预测模型已广泛应用于疾病的长期发病率预测。已经确定了几个参数和估计开发量化模型的预测能力，并比较新的模型与传统的模型。这些估计量一般没有考虑到通常可用于拟合模型的生存数据中的删失。本文解决了这个问题。考虑的主要参数是净重新分类改善（NRI）和综合歧视改善（IDI）。我们以前也考虑过一致性的主要指标，ROC曲线下面积（AUC），也称为c统计量。我们还考虑了人群归因风险（PAR）和最高五分之一风险与最低五分之一风险的预测风险之比。我们评估了这些不同的参数的估计与模拟研究，也适用于冠心病（CHD）的前瞻性研究。我们的模拟研究表明，在一般情况下，我们的估计有很小的偏差，更少的偏差和更小的方差比传统的估计。我们已经应用我们的方法来评估与没有该因素的模型相比，每个传统CHD风险因素的风险预测的改善。这些传统的风险因素被认为是有价值的，但是当将它们中的任何一个添加到忽略了一个因素的风险预测模型中时，对于任何参数的改进通常都很小。这一经验应该使我们做好准备，不要期望发现任何新的风险因素的风险预测改进评价参数的大值。版权所有（C）2010约翰威利父子有限公司22
Risk prediction models have been widely applied for the prediction of long-term incidence of disease. Several parameters have been identified and estimators developed to quantify the predictive ability of models and to compare new models with traditional models. These estimators have not generally accounted for censoring in the survival data normally available for fitting the models. This paper remedies that problem. The primary parameters considered are net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We have previously similarly considered a primary measure of concordance, area under the ROC curve (AUC), also called the c-statistic. We also include here consideration of population attributable risk (PAR) and ratio of predicted risk in the top quintile of risk to that in the bottom quintile. We evaluated estimators of these various parameters both with simulation studies and also as applied to a prospective study of coronary heart disease (CHD). Our simulation studies showed that in general our estimators had little bias, and less bias and smaller variances than the traditional estimators. We have applied our methods to assessing improvement in risk prediction for each traditional CHD risk factor compared to a model without that factor. These traditional risk factors are considered valuable, yet when adding any of them to a risk prediction model that has omitted the one factor, the improvement is generally small for any of the parameters. This experience should prepare us to not expect large values of the risk prediction improvement evaluation parameters for any new risk factor to be discovered. Copyright (C) 2010 John Wiley & Sons, Ltd. 22