Comparing first hitting time and proportional hazards regression models

Stogiannis, D; Caroni, C; Anagnostopoulos, CE; Toumpoulis, IK

dc.contributor.author	Stogiannis, D	en
dc.contributor.author	Caroni, C	en
dc.contributor.author	Anagnostopoulos, CE	en
dc.contributor.author	Toumpoulis, IK	en
dc.date.accessioned	2014-03-01T11:45:10Z
dc.date.available	2014-03-01T11:45:10Z
dc.date.issued	2011	en
dc.identifier.issn	0266-4763	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/37264
dc.subject	Cox regression	en
dc.subject	Cured fraction	en
dc.subject	First hitting time	en
dc.subject	Inverse gaussian	en
dc.subject	Proportional hazards	en
dc.subject	Survival analysis	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	INVERSE GAUSSIAN MODEL	en
dc.subject.other	THRESHOLD REGRESSION	en
dc.subject.other	SURVIVAL-DATA	en
dc.subject.other	EVENTS	en
dc.subject.other	LIFE	en
dc.title	Comparing first hitting time and proportional hazards regression models	en
heal.type	other	en
heal.identifier.primary	10.1080/02664763.2010.505954	en
heal.identifier.secondary	http://dx.doi.org/10.1080/02664763.2010.505954	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	Cox's widely used semi-parametric proportional hazards (PH) regression model places restrictions on the possible shapes of the hazard function. Models based on the first hitting time (FHT) of a stochastic process are among the alternatives and have the attractive feature of being based on a model of the underlying process.We review and compare the PH model and an FHT model based on aWiener process which leads to an inverse Gaussian (IG) regression model. This particular model can also represent a ""cured fraction"" or long-term survivors. A case study of survival after coronary artery bypass grafting is used to examine the interpretation of the IG model, especially in relation to covariates that affect both of its parameters. © 2011 Taylor & Francis.	en
heal.publisher	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD	en
heal.journalName	Journal of Applied Statistics	en
dc.identifier.doi	10.1080/02664763.2010.505954	en
dc.identifier.isi	ISI:000290406300013	en
dc.identifier.volume	38	en
dc.identifier.issue	7	en
dc.identifier.spage	1483	en
dc.identifier.epage	1492	en