Impact of correlation of predictors on discrimination of risk models in development and external populations.
Impact of correlation of predictors on discrimination of risk models in development and external populations.
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DOI：
10.1186/s12874-017-0345-1
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发表时间：
2017-04-19
影响因子：
4
通讯作者：
Ferket B
中科院分区：
文献类型：
--
作者：
Kundu S;Mazumdar M;Ferket B
已知风险模型的ROC曲线下面积（AUC）受预测因子的病例组合和效应大小差异的影响。然而，预测因子间相关性异质性的影响仍在研究中。我们试图评估预测因素之间的相关性如何影响发展中国家和外部人群的AUC。我们基于两个连续预测因子的均值、标准差和相关性，使用两种不同的方法模拟了假设的总体。在第一种方法中，预测因子的分布和相关性被假定为总体。在第二种方法中，这些参数以疾病状态为条件建模。在这两种方法中，都拟合了多变量逻辑回归模型来预测个体的疾病风险。在一个人群中建立的每个风险模型在其余人群中验证，以调查外部有效性。对于这两种方法，我们观察到发展种群和外部种群的AUC的大小取决于预测因子之间的相关性。根据预测因子效应的方向和模拟方法，在强正相关和负相关的情景下估计的auc较低。然而，当调整后的预测因子效应大小被指定为相反的方向时，负相关的增加持续提高了AUC。即使存在相似的预测效应，外部验证群体中的auc也高于或低于衍生队列。风险预测模型应在不同相关结构的外部人群中进行判别，以更好地推断模型的通用性。本文的在线版本（doi:10.1186/s12874-017-0345-1）包含补充材料，可供授权用户使用。
The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability. The online version of this article (doi:10.1186/s12874-017-0345-1) contains supplementary material, which is available to authorized users.