Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13352
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dc.contributor.authorBURZYKOWSKI, Tomasz-
dc.contributor.authorCarpenter, James-
dc.contributor.authorCoens, Corneel-
dc.contributor.authorEvans, Daniel-
dc.contributor.authorFrance, Lesley-
dc.contributor.authorKenward, Mike-
dc.contributor.authorLane, Peter-
dc.contributor.authorMatcham, James-
dc.contributor.authorPhillips, Alan-
dc.contributor.authorRoger, James-
dc.contributor.authorSullivan, Brian-
dc.contributor.authorWhite, Ian-
dc.contributor.authorYu, Ly-Mee-
dc.contributor.authorMorgan, David-
dc.date.accessioned2012-03-12T10:50:27Z-
dc.date.available2012-03-12T10:50:27Z-
dc.date.issued2010-
dc.identifier.citationPHARMACEUTICAL STATISTICS, 9 (4), p. 288-297-
dc.identifier.issn1539-1604-
dc.identifier.urihttp://hdl.handle.net/1942/13352-
dc.description.abstractThe Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop-outs, explaining the role and limitations of the ‘last observation carried forward’ method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one-day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro-active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan–Meier curves looking at time to withdrawal.-
dc.language.isoen-
dc.subject.othermissing data; LOCF; MMRM; multiple imputation-
dc.titleMissing data : discussion points from the PSI missing data expert group-
dc.typeJournal Contribution-
dc.identifier.epage297-
dc.identifier.issue4-
dc.identifier.spage288-
dc.identifier.volume9-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1002/pst.39-
item.accessRightsOpen Access-
item.contributorBURZYKOWSKI, Tomasz-
item.contributorCarpenter, James-
item.contributorCoens, Corneel-
item.contributorEvans, Daniel-
item.contributorFrance, Lesley-
item.contributorKenward, Mike-
item.contributorLane, Peter-
item.contributorMatcham, James-
item.contributorPhillips, Alan-
item.contributorRoger, James-
item.contributorSullivan, Brian-
item.contributorWhite, Ian-
item.contributorYu, Ly-Mee-
item.contributorMorgan, David-
item.fulltextWith Fulltext-
item.fullcitationBURZYKOWSKI, Tomasz; Carpenter, James; Coens, Corneel; Evans, Daniel; France, Lesley; Kenward, Mike; Lane, Peter; Matcham, James; Phillips, Alan; Roger, James; Sullivan, Brian; White, Ian; Yu, Ly-Mee & Morgan, David (2010) Missing data : discussion points from the PSI missing data expert group. In: PHARMACEUTICAL STATISTICS, 9 (4), p. 288-297.-
crisitem.journal.issn1539-1604-
crisitem.journal.eissn1539-1612-
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