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Survival analysis for longitudinal data

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dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2012-12-19T16:30:30Z
dc.date.available 2012-12-19T16:30:30Z
dc.date.issued 1999
dc.identifier.uri http://hdl.handle.net/10344/2757
dc.description peer-reviewed en_US
dc.description.abstract In longitudinal studies with a set of continuous or ordinal repeated response variables it may be convenient to summarise the outcome as a threshold event. Then, the time to this event becomes of interest. This is particularly true of recent Ophthalmological trials evaluating the effect of treatment on the loss of visual acuity over time. However, the practice of employing conventional survival analysis methods for testing the null hypothesis of no treatment effect in these types of studies is intrinsically flawed as the exact time to the threshold event is not measured. In this paper we obtain a general likelihood for the unknown parameters when the underlying sur- vival model is parametric. We also recover the actual information available in repeated measures data for a variety of models and compare the results with those obtained using a mis-specified model, which assumes the time to the event is one of the possibly irregularly spaced inspection times. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 14th International Workshop on Statistical Modelling
dc.relation.uri http://www.statmod.org/workshops.htm
dc.subject longitudinal data en_US
dc.subject survival analysis en_US
dc.subject model mis-specification en_US
dc.subject grouped likelihood en_US
dc.title Survival analysis for longitudinal data en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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