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Modelling high dimensional sets of binary co-morbidities

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dc.contributor.author Conde, Susana
dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2013-01-08T15:00:39Z
dc.date.available 2013-01-08T15:00:39Z
dc.date.issued 2007
dc.identifier.uri http://hdl.handle.net/10344/2791
dc.description peer-reviewed en_US
dc.description.abstract The construction of classical co-morbidity indices is described. When the co-morbidities are binary we advocate the use of log-linear models which better capture the dependence structure in the data. We use R to implement new search strategies which enable us to analyse, sparse, high dimensional contingency tables rapidly and hence identify the best fitting models. We apply our new algorithms to a set of real medical data. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 22nd International Workshop on Statistical Modelling;
dc.relation.uri http://www.statmod.org/workshops.htm
dc.subject co-morbidity index en_US
dc.subject binary data en_US
dc.subject hierarchical long-linear model en_US
dc.title Modelling high dimensional sets of binary co-morbidities 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.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1399830


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