Semiparametric methods for evaluating risk prediction markers in case-control studies
Semiparametric methods for evaluating risk prediction markers in case-control studies
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DOI：
10.1093/biomet/asp040
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发表时间：
2009-12-01
期刊：
影响因子：
2.7
通讯作者：
Pepe, Margaret Sullivan
中科院分区：
文献类型：
--
作者：
Huang, Ying;Pepe, Margaret Sullivan
对于二元疾病结果，一个校准良好的风险模型的性能可以用风险的人口分布来表征，并用预测曲线来显示。更好的表现的特点是更广泛的风险分布，因为这对应于更好的风险分层，即更多的受试者被确定为疾病结果的低风险和高风险。虽然已经开发了从队列研究中估计预测曲线的方法，但大多数评估新型风险预测标志物的研究采用病例对照设计。在这里，我们开发了适应病例对照数据的半参数方法。半参数方法是灵活的，并且自然地推广了以前为队列数据开发的方法。前列腺癌风险预测标志物的应用说明了这种方法。
The performance of a well-calibrated risk model for a binary disease outcome can be characterized by the population distribution of risk and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of risk, since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the disease outcome. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric methods that accommodate case-control data. The semiparametric methods are flexible, and naturally generalize methods previously developed for cohort data. Applications to prostate cancer risk prediction markers illustrate the methods.