Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models
Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models
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DOI：
10.1002/sim.6927
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发表时间：
2016-07-10
影响因子：
2
通讯作者：
Michiels, Stefan
中科院分区：
文献类型：
--
作者：
Ternes, Nils;Rotolo, Federico;Michiels, Stefan
随着生物数据的维度越来越高，在多种候选物中正确选择预后生物标志物正变得越来越具有挑战性。因此，最小化错误发现率（FDR）是最重要的，而低假阴性率（FNR）是一种补充措施。套索法是Cox回归中常用的一种选择方法，但其结果严重依赖于惩罚参数。通常使用最大交叉验证对数似然（max-cvl）来选择。然而，这种方法通常有很高的FDR。我们回顾的方法更保守的选择。我们通过添加惩罚项来提出cvl的经验扩展，该惩罚项在拟合优度和模型的简约性之间进行权衡，导致选择更少的生物标记物，并且正如我们所示，在FNR大幅增加的情况下减少了FDR。我们进行了零和中等稀疏备选方案的模拟研究，并将我们的方法与标准套索和其他10个竞争对手进行了比较：Akaike信息准则（AIC）、修正AIC、贝叶斯信息准则（BIC）、扩展BIC、Hannan和Quinn信息准则（HQIC）、风险信息准则（RIC）、单标准误差规则、自适应套索、稳定性选择和百分点套索。我们的扩展在降低FDR和有限提高FNR之间实现了所有场景的最佳折衷，其次是AIC、RIC和自适应套索，它们在某些情况下表现良好。我们使用523例乳腺癌患者的基因表达数据来说明方法。总之，我们建议，只要目标是严格的FDR和有限的FNR，就将我们的扩展应用于套索。版权所有：John Wiley & Sons， Ltd。
Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter . Usually, is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of . We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright (c) 2016 John Wiley & Sons, Ltd.