Graphical tests for the assumption of Gamma and Inverse Gaussian frailty distributions

Economou, P; Caroni, C

dc.contributor.author	Economou, P	en
dc.contributor.author	Caroni, C	en
dc.date.accessioned	2014-03-01T01:22:27Z
dc.date.available	2014-03-01T01:22:27Z
dc.date.issued	2005	en
dc.identifier.issn	1380-7870	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16575
dc.subject	Burr	en
dc.subject	Diagnostic plots	en
dc.subject	Frailty	en
dc.subject	Generalized Inverse Gaussian	en
dc.subject	Proportional hazards	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	article	en
dc.subject.other	Greece	en
dc.subject.other	human	en
dc.subject.other	life table	en
dc.subject.other	normal distribution	en
dc.subject.other	proportional hazards model	en
dc.subject.other	statistics	en
dc.subject.other	survival	en
dc.subject.other	Greece	en
dc.subject.other	Humans	en
dc.subject.other	Life Tables	en
dc.subject.other	Normal Distribution	en
dc.subject.other	Proportional Hazards Models	en
dc.subject.other	Statistics	en
dc.subject.other	Survival Analysis	en
dc.title	Graphical tests for the assumption of Gamma and Inverse Gaussian frailty distributions	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s10985-005-5240-0	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s10985-005-5240-0	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	The common choices of frailty distribution in lifetime data models include the Gamma and Inverse Gaussian distributions. We present diagnostic plots for these distributions when frailty operates in a proportional hazards framework. Firstly, we present plots based on the form of the unconditional survival function when the baseline hazard is assumed to be Weibull. Secondly, we base a plot on a closure property that applies for any baseline hazard, namely, that the frailty distribution among survivors at time t has the same form as the original distribution, with the same shape parameter but different scale parameter. We estimate the shape parameter at different values of t and examine whether it is constant, that is, whether plotted values form a straight line parallel to the time axis. We provide simulation results assuming Weibull baseline hazard and an example to illustrate the methods. © 2005 Springer Science+Business Media, Inc.	en
heal.publisher	SPRINGER	en
heal.journalName	Lifetime Data Analysis	en
dc.identifier.doi	10.1007/s10985-005-5240-0	en
dc.identifier.isi	ISI:000233392800008	en
dc.identifier.volume	11	en
dc.identifier.issue	4	en
dc.identifier.spage	565	en
dc.identifier.epage	582	en