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DC Field | Value | Language |
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dc.contributor.author | BEUNCKENS, Caroline | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | Kenward, Michael G. | - |
dc.date.accessioned | 2007-11-11T09:48:06Z | - |
dc.date.available | 2007-11-11T09:48:06Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | CLINICAL TRIALS, 2(5). p. 379-386 | - |
dc.identifier.issn | 1740-7745 | - |
dc.identifier.uri | http://hdl.handle.net/1942/2063 | - |
dc.description.abstract | Background In many clinical trials, data are collected longitudinally overtime. In such studies, missingness, in particular dropout, is an often encountered phenomenon. Methods We discuss commonly used but often problematic methods such as complete case analysis and last observation carried forward and contrast them with broadly valid and easy to implement direct-likelihood methods. We comment on alternatives such as multiple imputation and the expectation-maximization algorithm. Results We apply these methods in particular to data from a study with continuous outcomes. The outcomes are modelled using a general linear mixed-effects model. The bias with CC and LOCF is established in the case study and the advantages of the direct-likelihood approach shown. Conclusions We have established formal but easy to understand arguments for a shift towards a direct-likelihood paradigm when analysing incomplete data from longitudinal clinical trials, necessitating neither imputation nor deletion. | - |
dc.description.sponsorship | Caroline Beunckens and Geert Molenberghs gratefully acknowledge support from Fonds Weten-schappelijk Onderzoek- Vlaanderen Research Project G.0002.98 "Sensitivity Analysis for Incomplete and Coarse Data" and from Belgian IUAP/I-'AI network "Statistical echniques and Modeling for Complex Substantive Questions with Complex Data". | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | HODDER ARNOLD, HODDER HEADLINE PLC | - |
dc.rights | © Society for Clinical Trials 2005 | - |
dc.title | Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 386 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 379 | - |
dc.identifier.volume | 2 | - |
local.format.pages | 8 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Limburgs Univ Ctr, Ctr Stat, B-3590 Diepenbeek, Belgium. London Sch Hyg & Trop Med, London WC1, England.Molenberghs, G, Limburgs Univ Ctr, Ctr Stat, Bldg D, B-3590 Diepenbeek, Belgium.geert.molenberghs@luc.ac.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1191/1740774505cn119oa | - |
dc.identifier.isi | 000233178800001 | - |
item.fullcitation | BEUNCKENS, Caroline; MOLENBERGHS, Geert & Kenward, Michael G. (2005) Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials. In: CLINICAL TRIALS, 2(5). p. 379-386. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2006 | - |
item.contributor | BEUNCKENS, Caroline | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | Kenward, Michael G. | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 1740-7745 | - |
crisitem.journal.eissn | 1740-7753 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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beunckens2005.pdf Restricted Access | Published version | 1.66 MB | Adobe PDF | View/Open Request a copy |
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