Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

Ying-Ju Chen; Wei Ning; Arjun K Gupta

doi:10.1002/pst.1744

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

Pharm Stat. 2016 May;15(3):246-54. doi: 10.1002/pst.1744. Epub 2016 Mar 3.

Authors

Ying-Ju Chen¹, Wei Ning², Arjun K Gupta²

Affiliations

¹ Department of Information Systems, Analytics, Miami University, Oxford, OH, 45056, USA.
² Department of Mathematics Statistics, Bowling Green State University, Bowling Green, OH, 43403, USA.

PMID: 26936529
DOI: 10.1002/pst.1744

Abstract

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.

Keywords: change point; empirical likelihood; mean residual life; right censored data.

MeSH terms

Age Factors
Data Interpretation, Statistical*
Humans
Likelihood Functions
Models, Statistical*
Sex Factors
Survival Analysis*
Time Factors