A real survival analysis application via variable selection methods for Cox's proportional hazards model

Androulakis, E; Koukouvinos, C; Mylona, K; Vonta, F

dc.contributor.author	Androulakis, E	en
dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Mylona, K	en
dc.contributor.author	Vonta, F	en
dc.date.accessioned	2014-03-01T01:32:32Z
dc.date.available	2014-03-01T01:32:32Z
dc.date.issued	2010	en
dc.identifier.issn	0266-4763	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20171
dc.subject	Cox's proportional hazards model	en
dc.subject	High-dimensional dataset	en
dc.subject	Nonconcave penalized likelihood	en
dc.subject	Survival analysis	en
dc.subject	Trauma	en
dc.subject	Variable selection	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	ORACLE PROPERTIES	en
dc.subject.other	LASSO	en
dc.subject.other	LIKELIHOOD	en
dc.title	A real survival analysis application via variable selection methods for Cox's proportional hazards model	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/02664760903038406	en
heal.identifier.secondary	http://dx.doi.org/10.1080/02664760903038406	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Variable selection is fundamental to high-dimensional statistical modeling in diverse fields of sciences. In our health study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and 111 factors that include demographic, transport, and clinical data. The statistical methods employed in this work are the non- concave penalized likelihood methods, Smoothly Clipped Absolute Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the maximum partial likelihood estimation method, and the best subset variable selection, adjusted to Cox's proportional hazards model and used to detect possible risk factors, which affect the length of stay in a hospital. A variety of different statistical models are considered, with respect to the combinations of factors while censored observations are present. A comparative survey reveals several differences between results and execution times of each method. Finally, we provide useful biological justification of our results. © 2010 Taylor & Francis.	en
heal.publisher	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD	en
heal.journalName	Journal of Applied Statistics	en
dc.identifier.doi	10.1080/02664760903038406	en
dc.identifier.isi	ISI:000280810900011	en
dc.identifier.volume	37	en
dc.identifier.issue	8	en
dc.identifier.spage	1399	en
dc.identifier.epage	1406	en