在可能存在非比例风险的情况下，对微阵列生存研究中的基因选择。
Gene selection in microarray survival studies under possibly non-proportional hazards.
机构信息
Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.
出版信息
Bioinformatics. 2010 Mar 15;26(6):784-90. doi: 10.1093/bioinformatics/btq035. Epub 2010 Jan 29.
MOTIVATION
Univariate Cox regression (COX) is often used to select genes possibly linked to survival. With non-proportional hazards (NPH), COX could lead to under- or over-estimation of effects. The effect size measure c=P(T(1)<T(0)), i.e. the probability that a person randomly chosen from group G(1) dies earlier than a person from G(0), is independent of the proportional hazards (PH) assumption. Here we consider its generalization to continuous data c' and investigate the suitability of c' for gene selection.
RESULTS
Under PH, c' is most efficiently estimated by COX. Under NPH, c' can be obtained by weighted Cox regression (WHE) or a novel method, concordance regression (CON). The least biased and most stable estimates were obtained by CON. We propose to use c' as summary measure of effect size to rank genes irrespective of different types of NPH and censoring patterns.
AVAILABILITY
WHE and CON are available as R packages.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary Data are available at Bioinformatics online.
动机
单变量 Cox 回归（COX）常用于选择可能与生存相关的基因。在非比例风险（NPH）情况下，COX 可能导致效应的低估或高估。效应大小度量 c=P(T(1)<T(0))，即从组 G(1)中随机选择的人的死亡时间早于从组 G(0)中随机选择的人的概率，与比例风险（PH）假设无关。在这里，我们考虑将其推广到连续数据 c'，并研究 c' 用于基因选择的适用性。
结果
在 PH 下，c' 可以通过 COX 最有效地估计。在 NPH 下，c' 可以通过加权 Cox 回归（WHE）或一种新方法，一致性回归（CON）获得。CON 得到的估计是最无偏和最稳定的。我们建议使用 c' 作为效应大小的综合度量指标，对基因进行排名，而不考虑不同类型的 NPH 和删失模式。
可用性
WHE 和 CON 作为 R 包提供。
联系方式
补充信息
补充数据可在生物信息学在线获得。
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