Show simple item record

dc.contributor.authorNg, Shu Kay
dc.contributor.authorHolden, Libby
dc.contributor.authorSun, Jing
dc.contributor.editorRalph D'Agostino
dc.date.accessioned2017-05-03T15:17:59Z
dc.date.available2017-05-03T15:17:59Z
dc.date.issued2012
dc.date.modified2012-08-08T00:04:47Z
dc.identifier.issn0277-6715
dc.identifier.doi10.1002/sim.5426
dc.identifier.urihttp://hdl.handle.net/10072/46084
dc.description.abstractIdentification of co-morbidity patterns of health conditions is critical for evidence- based practice to improve the prevention, treatment, and health care of relevant diseases. Existing approaches focus mainly on either using descriptive measures of co-morbidity in terms of the prevalence of co-existing conditions, or addressing the prevalence of co-morbidity based on a particular disease (e.g. psychosis) or a specific population (e.g. hospital patients). As coincidental co-morbidity by chance increases with the prevalence rates of the conditions, which in turn depend heavily on the population under study, research findings on co-morbidity patterns using those approaches may provide unreliable results. In this paper, we propose an asymmetric version of Somers' D statistic to provide a quantitative measure of co-morbidity that accounts for co-occurrence of conditions by chance, and develop a unified clustering algorithm to identify co-morbidity patterns with adjustment for multiple testing and control for the false discovery rate. We assess the applicability of the proposed co-morbidity measure and investigate the performance of the proposed procedure for the adjustment of multiple testing by conducting a comparative study and a sensitivity analysis, respectively. The proposed method is illustrated using a national survey data set of mental health and wellbeing and a national health survey data set in Australia. Keywords: asymmetric Somers' D statistic; co-morbidity; concordance statistic; multiplicity problem; national survey data; overlapping clusters.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherJohn Wiley & Sons
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom3393
dc.relation.ispartofpageto3405
dc.relation.ispartofissue27
dc.relation.ispartofjournalStatistics in Medicine
dc.relation.ispartofvolume31
dc.rights.retentionY
dc.subject.fieldofresearchBiostatistics
dc.subject.fieldofresearchEpidemiology
dc.subject.fieldofresearchStatistics
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchcode010402
dc.subject.fieldofresearchcode111706
dc.subject.fieldofresearchcode0104
dc.subject.fieldofresearchcode1117
dc.titleIdentifying co-morbidity patterns of health conditions via cluster analysis of pairwise concordance statistics
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Health, School of Medicine
gro.date.issued2012
gro.hasfulltextNo Full Text
gro.griffith.authorHolden, Libby
gro.griffith.authorSun, Jing
gro.griffith.authorNg, Shu Kay Angus


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record