Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data
Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data
复制标题
DOI：
10.1093/bioinformatics/bti324
复制
发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Luan, YH
中科院分区：
文献类型：
--
作者：
Li, HZ;Luan, YH
动机：后基因组学时代的一个重要研究领域是将高维遗传或基因组数据与患者的各种临床表型相关联。由于患者中某些临床事件的时间变异性较大，研究可能删失的生存表型可能比将表型作为分类变量处理更能提供信息。由于高维和删失，建立事件发生时间的预测模型比分类/线性回归问题更困难。我们建议开发一个使用平滑样条估计一般比例风险模型的助推程序。这样一个程序可以潜在地用于识别非线性影响的基因上的风险发展的events.Results：我们的实证模拟研究表明，该程序确实可以恢复真正的功能形式的协变量，并可以识别重要的变量，是相关的风险事件。从弥漫性大B细胞淋巴瘤患者化疗后的生存预测结果表明，所提出的方法可用于识别与癌症死亡时间相关的重要基因，并建立一个预测未来患者生存的简约模型。此外，有明确的证据表明，一些基因对生存时间的非线性影响。
Motivation: An important area of research in the postgenomics era is to relate high-dimensional genetic or genomic data to various clinical phenotypes of patients. Due to large variability in time to certain clinical events among patients, studying possibly censored survival phenotypes can be more informative than treating the phenotypes as categorical variables. Due to high dimensionality and censoring, building a predictive model for time to event is more difficult than the classification/linear regression problem. We propose to develop a boosting procedure using smoothing splines for estimating the general proportional hazards models. Such a procedure can potentially be used for identifying non-linear effects of genes on the risk of developing an event.Results: Our empirical simulation studies showed that the procedure can indeed recover the true functional forms of the covariates and can identify important variables that are related to the risk of an event. Results from predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed method can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. In addition, there is clear evidence of non-linear effects of some genes on survival time.