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Multi-parameter regression survival models

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dc.contributor.author Burke, Kevin
dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2013-01-10T11:44:47Z
dc.date.available 2013-01-10T11:44:47Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10344/2801
dc.description peer-reviewed en_US
dc.description.abstract It is well known that the proportional hazards (PH) assumption is a simplifying assumption in survival analysis that may not always be appropriate. However, PH models are routinely fitted and inference is made on the data based on such models. A major flaw here is that if the data are non-PH then we will reach incorrect conclusions by making this assumption. For example we may find a covariate to be statistically insigni cant when in fact it is important, but the model fails to pick this up. Even if a PH model does pick up the statistical significance of a non-PH covariate, the nature of the effect of the covariate on survival, as determined by this simplistic model, will clearly be incorrect. We introduce a regression-based extension of PH modelling to try an account for situations such as those described above and offer new, previously unavailable insights, into the data. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 27th International Workshop on Statistical Modelling;
dc.relation.uri http://www.statmod.org/workshops.htm
dc.subject crossing hazards en_US
dc.subject multi-parameter regression survival models en_US
dc.subject PH and non-PH models en_US
dc.title Multi-parameter regression survival models en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.contributor.sponsor IRCSET en_US
dc.relation.projectid 07/MI012 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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