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

Available online: 09 Nov 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article