A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.
A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.
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DOI：
10.1002/gepi.21600
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发表时间：
2012-01
影响因子：
2.1
通讯作者：
Xu, Jianfeng
中科院分区：
文献类型：
--
作者：
Newcombe, Paul J.;Reck, Brian H.;Sun, Jielin;Platek, Greg T.;Verzilli, Claudio;Kader, A. Karim;Kim, Seong-Tae;Hsu, Fang-Chi;Zhang, Zheng;Zheng, S. Lilly;Mooser, Vincent E.;Condreay, Lynn D.;Spraggs, Colin F.;Whittaker, John C.;Rittmaster, Roger S.;Xu, Jianfeng
关键词：
除了目前使用的临床变量外，我们还对遗传学的预测益处进行了迄今为止最全面的比较，使用了来自度他雄胺减少前列腺癌事件（REDUCE®）试验安慰剂组的1，547名白人男性的33个单核苷酸多态性（SNP）的基因型数据。此外，我们对将遗传学纳入临床风险预测的三种技术进行了详细比较。第一种方法是标准逻辑回归模型，其包括临床协变量和每个遗传标记的单独项。这种方法忽略了大量的外部信息的影响大小，这些全基因组关联研究（GWAS）复制的SNP。第二种和第三种方法研究了两种可能的方法来合并荟萃分析的外部SNP效应估计值-一种是通过仅基于Meta分析估计值的加权PCa“风险”评分，另一种是通过贝叶斯逻辑回归模型中的信息先验将当前和先验数据合并。所有方法都证明了在并入遗传学后预测性能的轻微改善。结合外部信息的两种方法显示最大的受试者操作特征AUC从0.61增加到0.64。我们的方法比较的价值可能在于观察到的性能相似性，而不是差异，三种方法之间的资源需求非常不同。包括外部信息的两种方法表现最好，但尽管复杂性有很大差异，但也只是微不足道。
We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates – one via a weighted PCa ‘risk’ score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.
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