Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/339
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCurran, Desmond-
dc.contributor.authorBacchi, M.-
dc.contributor.authorHsu Schmitz, Shu-Fang-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorSYLVESTER, Richard-
dc.date.accessioned2004-10-22T14:42:04Z-
dc.date.available2004-10-22T14:42:04Z-
dc.date.issued1998-
dc.identifier.citationStatistics in Medicine, 17(5-7). p. 739-756-
dc.identifier.issn0277-6715-
dc.identifier.urihttp://hdl.handle.net/1942/339-
dc.description.abstractThis paper discusses methods of identifying the types of missingness in quality of life (QOL) data in cancer clinical trials. The first approach involves collecting information on why the QOL questionnaires were not completed. Based on the reasons provided one may be able to distinguish the mechanisms causing missing data. The second approach is to model the missing data mechanism and perform hypothesis testing to determine the missing data processes. Two methods of testing if missing data are missing completely at random (MCAR) are presented and applied to incomplete longitudinal QOL data obtained from international multi-centre cancer clinical trials. The first method (Ridout, 1991) is based on a logistic regression and the second method (Park and Davis, 1993) is based on an adaptation of weighted least squares. In one application (advanced breast cancer) missing data was not likely to be MCAR. In the second application (adjuvant breast cancer) the missing mechanism was dependent on the QOL scale under study. MCAR and missing at random (MAR) have distinct consequences for data analysis. Therefore it is relevant to distinguish between them. However, if either MCAR or MAR hold, likelihood or Bayesian inferences can be based solely on the observed data, although for MAR, depending on the research question, modelling the dropout mechanism may still be necessary. Distinguishing between MAR and missing not at random (MNAR) is not trivial and relies on fundamentally untestable assumptions. © 1998 John Wiley & Sons, Ltd.-
dc.language.isoen-
dc.rights(c) 1998 John Wiley & Sons, Ltd-
dc.subjectClinical trials-
dc.subjectMissing data-
dc.titleIdentifying the types of missingness in quality of life data from clinical trials-
dc.typeJournal Contribution-
dc.identifier.epage756-
dc.identifier.issue5-7-
dc.identifier.spage739-
dc.identifier.volume17-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1002/(SICI)1097-0258(19980315/15)17:5/7<739::AID-SIM818>3.3.CO;2-D-
dc.identifier.isi000072447800021-
item.validationecoom 1999-
item.fulltextWith Fulltext-
item.contributorHsu Schmitz, Shu-Fang-
item.contributorMOLENBERGHS, Geert-
item.contributorCurran, Desmond-
item.contributorSYLVESTER, Richard-
item.contributorBacchi, M.-
item.fullcitationCurran, Desmond; Bacchi, M.; Hsu Schmitz, Shu-Fang; MOLENBERGHS, Geert & SYLVESTER, Richard (1998) Identifying the types of missingness in quality of life data from clinical trials. In: Statistics in Medicine, 17(5-7). p. 739-756.-
item.accessRightsRestricted Access-
crisitem.journal.issn0277-6715-
crisitem.journal.eissn1097-0258-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
Curran_et_al-2015-Statistics_in_Medicine (1).pdf
  Restricted Access
Published version267.6 kBAdobe PDFView/Open    Request a copy
Show simple item record

WEB OF SCIENCETM
Citations

66
checked on Jun 29, 2022

Page view(s)

60
checked on Jun 29, 2022

Download(s)

40
checked on Jun 29, 2022

Google ScholarTM

Check

Altmetric


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