Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/27627
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBRUCKERS, Liesbeth-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorPulinx, Bianca-
dc.contributor.authorHellenthal, Femina-
dc.contributor.authorSchurink, Geert-
dc.date.accessioned2019-01-23T10:25:35Z-
dc.date.available2019-01-23T10:25:35Z-
dc.date.issued2018-
dc.identifier.citationJOURNAL OF BIOPHARMACEUTICAL STATISTICS, 28(5), p. 983-1004-
dc.identifier.issn1054-3406-
dc.identifier.urihttp://hdl.handle.net/1942/27627-
dc.description.abstractDegeneration of the aortic wall becomes life-threatening when the risk of rupture increases. Cluster analysis on repeated measures of the diameter of the artery revealed two subgroups of patients included in a surveillance program. These results were obtained under the assumption of missingness at random. In this article, we study the vulnerability of the cluster analysis results - the estimated trajectories and the posterior membership probabilities - by applying different missing-data models for non-ignorable dropout, as proposed by Muthen et al. (2011) to the growth of the diameter of the artery.-
dc.description.sponsorshipThe authors gratefully acknowledge support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy). We are grateful to the Department of Clinical Chemistry, Maastricht University Medical Center (MUMC), Maastricht, for kind permission to use the AAA data.-
dc.language.isoen-
dc.publisherTAYLOR & FRANCIS INC-
dc.subject.otherDistal event; incomplete data; latent-class growth models; pattern-mixture models; selection models; sensitivity analysis-
dc.subject.otherDistal event; incomplete data; latent-class growth models; pattern-mixture models; selection models; sensitivity analysis-
dc.titleCluster analysis for repeated data with dropout: Sensitivity analysis using a distal event-
dc.typeJournal Contribution-
dc.identifier.epage1004-
dc.identifier.issue5-
dc.identifier.spage983-
dc.identifier.volume28-
local.format.pages22-
local.bibliographicCitation.jcatA1-
dc.description.notes[Bruckers, Liesbeth; Molenberghs, Geert] Univ Hasselt, I BioStat, Agoralaan, B-3500 Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Leuven, Belgium. [Pulinx, Bianca; Hellenthal, Femina; Schurink, Geert] Maastricht Univ, Med Ctr, Dept Clin Chem, Maastricht, Netherlands.-
local.publisher.placePHILADELPHIA-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1080/10543406.2018.1428612-
dc.identifier.isi000443990700013-
item.fulltextWith Fulltext-
item.contributorBRUCKERS, Liesbeth-
item.contributorMOLENBERGHS, Geert-
item.contributorPulinx, Bianca-
item.contributorHellenthal, Femina-
item.contributorSchurink, Geert-
item.fullcitationBRUCKERS, Liesbeth; MOLENBERGHS, Geert; Pulinx, Bianca; Hellenthal, Femina & Schurink, Geert (2018) Cluster analysis for repeated data with dropout: Sensitivity analysis using a distal event. In: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 28(5), p. 983-1004.-
item.accessRightsOpen Access-
item.validationecoom 2019-
crisitem.journal.issn1054-3406-
crisitem.journal.eissn1520-5711-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
bruckers2018.pdf
  Restricted Access		Published version2.48 MBAdobe PDFView/Open    Request a copy
artikel_sensitivity_revision_december2017.pdfPeer-reviewed author version1.18 MBAdobe PDFView/Open
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.