City Research Online

Abstract

There are well-established survival analysis methodologies for data sets that are complete, with accurate information on censoring. But what if they are not complete? In this article we consider how to analyze cases where “hidden censoring” occurs, where individuals have effectively left the study but the hospital is unaware of this. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a breast cancer data set from the Kurdistan region of Iraq.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Theory and Practice on 22nd February 2016, available online: http://www.tandfonline.com/10.1080/15598608.2016.1152205
Publisher Keywords:	Survival analysis, censored data, Markov chain, breast cancer, Kurdistan, 62N01
Subjects:	Q Science > QA Mathematics
Departments:	School of Science & Technology > Mathematics
SWORD Depositor:	Symplectic Administrator

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