title: Gene selection for survival data under dependent censoring: A copula-based approach.
abstract: Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-based framework to investigate the bias caused by dependent censoring on gene selection. Then, we utilize the copula-based dependence model to develop an alternative gene selection procedure. Simulations show that the proposed procedure adjusts for the effect of dependent censoring and thus outperforms the existing method when dependent censoring is indeed present. The non-small-cell lung cancer data are analyzed to demonstrate the usefulness of our proposal. We implemented the proposed method in an R "compound.Cox" package.
authors.affiliation: Graduate Institute of Statistics, National Central University, Jhongli, Taiwan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Emura
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan yhchen@stat.sinica.edu.tw.
authors.forename: Yi-Hau
authors.initials: YH
authors.lastname: Chen
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016016:Proportional Hazards Models; D012306:Risk
keywords: Cox regression; Kendall’s tau; bivariate survival distribution; competing risk; compound covariate prediction; cross-validation