Correlation-adjusted regression survival scores for high-dimensional variable selection
Correlation-adjusted regression survival scores for high-dimensional variable selection
复制标题
DOI：
10.1002/sim.8116
复制
发表时间：
2019-06-15
影响因子：
2
通讯作者：
Schmid, Matthias
中科院分区：
文献类型：
--
作者：
Welchowski, Thomas;Zuber, Verena;Schmid, Matthias
背景：个性化医疗分类方法的发展高度依赖于预测性遗传标记的识别。在生存分析中，常常需要区分有影响和无影响的标志物。通常使用Cox评分进行单变量筛选，该评分量化了生存与每个标记之间的关联，以提供排名。由于考克斯评分没有考虑到标记之间的依赖关系，因此在存在高度相关的标记时，它们的使用是次优的。方法：作为Cox评分的替代方法，我们提出了相关调整回归生存（CARS）评分，用于右删减生存结果。通过去除标记之间的相关性，CARS评分量化了结果与“去相关”标记值集之间的关联。分数的估计基于逆概率加权，该加权应用于对数变换的事件时间。对于高维数据，估计是基于收缩技术。结果：在轻度规律性条件下，证明了CARS评分的一致性。在高相关性的模拟中，基于CARS评分排名的生存模型在准确率-召回率曲线下获得了比竞争方法更高的区域。在前列腺癌和乳腺癌上的两个应用实例证实了这些结果。CARS分数是在R包carSurv中实现的。结论：在涉及高维遗传数据的研究应用中，即使协变量之间的相关性较低，使用CARS评分进行标记选择也是Cox评分的有利选择。CARS评分具有简单易懂的解释和较低的计算要求，是个性化医学研究中易于使用的筛选工具。
Background: The development of classification methods for personalized medicine is highly dependent on the identification of predictive genetic markers. In survival analysis, it is often necessary to discriminate between influential and noninfluential markers. It is common to perform univariate screening using Cox scores, which quantify the associations between survival and each of the markers to provide a ranking. Since Cox scores do not account for dependencies between the markers, their use is suboptimal in the presence of highly correlated markers. Methods: As an alternative to the Cox score, we propose the correlation-adjusted regression survival (CARS) score for right-censored survival outcomes. By removing the correlations between the markers, the CARS score quantifies the associations between the outcome and the set of "decorrelated" marker values. Estimation of the scores is based on inverse probability weighting, which is applied to log-transformed event times. For high-dimensional data, estimation is based on shrinkage techniques. Results: The consistency of the CARS score is proven under mild regularity conditions. In simulations with high correlations, survival models based on CARS score rankings achieved higher areas under the precision-recall curve than competing methods. Two example applications on prostate and breast cancer confirmed these results. CARS scores are implemented in the R package carSurv. Conclusions: In research applications involving high-dimensional genetic data, the use of CARS scores for marker selection is a favorable alternative to Cox scores even when correlations between covariates are low. Having a straightforward interpretation and low computational requirements, CARS scores are an easy-to-use screening tool in personalized medicine research.