摘要： 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. 其他題名： Stat Methods Med Res 出版者： London, England: SAGE Publications 出版日期： 2016-12-01 出處： Statistical Methods in Medical Research, 2016-12, Vol.25 (6), p.2840-2857 資源來源： Sage Journals All Titles 版權： The Author(s) 2014 版權： The Author(s) 2014. 識別號： ISSN: 0962-2802 識別號： ISSN: 1477-0334 識別號： EISSN: 1477-0334 識別號： DOI: 10.1177/0962280214533378 識別號： PMID: 24821000
