Additive models for geo-referenced failure time data

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Show simple item record Ganguli, Bhaswati en_US Wand, Matt en_US
dc.contributor.editor en_US 2011-02-07T06:18:07Z 2011-02-07T06:18:07Z 2006 en_US
dc.identifier 2010000112 en_US
dc.identifier.citation Ganguli Bhaswati and Wand Matt 2006, 'Additive models for geo-referenced failure time data', John Wiley & Sons Ltd., vol. 25, no. 14, pp. 2469-2482. en_US
dc.identifier.issn 0277-6715 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.description.abstract Asthma researchers have found some evidence that geographical variations in susceptibility to asthma could reflect the effect of community level factors such as exposure to violence. Our methodology was motivated by a study of age at onset of asthma among children of inner-city neighbourhoods in East Boston. Cox's proportional hazards model was not appropriate since there was not enough information about the nature of geographical variations so as to impose a parametric relationship. In addition, some of the known risk factors were believed to have non-linear log-hazard ratios. We extend the geoadditive models of Kamman and Wand to the case where the outcome measure is a possibly censored time to event. We reduce the problem to one of fitting a Poisson mixed model by using Poisson approximations in conjunction with a mixed model formulation of generalized additive modelling. Our method allows for low-rank additive modelling, provides likelihood-based estimation of all parameters including the amount of smoothing and can be implemented using standard software. We illustrate our method on the East Boston data. en_US
dc.language en_US
dc.publisher John Wiley & Sons Ltd. en_US
dc.relation.isbasedon en_US
dc.title Additive models for geo-referenced failure time data en_US
dc.parent Statistics in Medicine en_US
dc.journal.volume 25 en_US
dc.journal.number 14 en_US
dc.publocation United Kingdom en_US
dc.identifier.startpage 2469 en_US
dc.identifier.endpage 2482 en_US SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000064878 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords * additive models; * disease mapping; * failure time data; * geostatistics; * mixed models; * penalized splines en_US
dc.staffid 110509 en_US

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