Title: Penalised Cox regression models for survival data
Authors: Duo Zhou; Dinesh P. Mital; Shankar Srinivasan; Masayuki Shibata
Addresses: School of Health Related Professions, Rutgers University, 65 Bergen Street Newark, NJ 07107-1709, USA ' School of Health Related Professions, Rutgers University, 65 Bergen Street Newark, NJ 07107-1709, USA ' School of Health Related Professions, Rutgers University, 65 Bergen Street Newark, NJ 07107-1709, USA ' School of Health Related Professions, Rutgers University, 65 Bergen Street Newark, NJ 07107-1709, USA
Abstract: In recent years, the advances in genetic technology have enabled the collection of large amount of genetic information; as a result, analysis tools for microarray data have been in high demand. Among all types of data, survival outcome is particularly of more interest to study. Such data has become very typical in biomedical research or genetic lab. However, typical statistical models do not work for such scenarios; in this paper, several Cox model-based penalised regression approaches will be evaluated.
Keywords: Cox proportional hazard model; nonlinear; survival data; prediction errors; AUCs; time-dependent ROC curves; receiver operating curve; lasso Cox model; ridge Cox model; elastic-net Cox model; penalised Cox regression models; microarray data analysis; bioinformatics; simulation.
DOI: 10.1504/IJMEI.2017.080920
International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.1, pp.1 - 19
Received: 14 Nov 2014
Accepted: 29 Feb 2016
Published online: 12 Dec 2016 *