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http://hdl.handle.net/1942/2208
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DC Field | Value | Language |
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dc.contributor.author | Mallinckrodt, Craig H. | - |
dc.contributor.author | Watkin, John G. | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | Carroll, Raymond J. | - |
dc.date.accessioned | 2007-11-12T08:13:45Z | - |
dc.date.available | 2007-11-12T08:13:45Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | PHARMACEUTICAL STATISTICS, 3(3). p. 161-169 | - |
dc.identifier.issn | 1539-1604 | - |
dc.identifier.uri | http://hdl.handle.net/1942/2208 | - |
dc.description.abstract | Missing data, and the bias they can cause, are an almost ever-present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood-based, mixed-effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF A NOVA as the primary analysis and focus on likelihood-based, mixed-effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright (C) 2004 John Wiley Sons Ltd. | - |
dc.description.sponsorship | Geert Molenberghs acknowledges support from the FWO-Vlaanderen Research Project ‘Sensitivity Analysis for Incomplete and Coarse Data’ and the Belgian IUAP/PAI network ‘Statistical Techniques and Modelling for Complex Substantive Questions with Complex Data’. Raymond Carroll acknowledges support from grants from the National Cancer Institute (CA57030) and the Texas A&M Center for Environmental and Rural Health via the National Institute of Environmental Health Sciences (P30-ES09106). | - |
dc.language.iso | en | - |
dc.publisher | JOHN WILEY & SONS INC | - |
dc.rights | (C) 2004 John Wiley & Sons, Ltd. | - |
dc.subject.other | missing data; longitudinal data; maximum likelihood; mixed-effects models | - |
dc.subject.other | missing data; longitudinal data; maximum likelihood; mixed-effects models | - |
dc.title | Choice of the primary analysis in longitudinal clinical trials | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 169 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 161 | - |
dc.identifier.volume | 3 | - |
local.format.pages | 9 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA. Limburgs Univ Ctr, Ctr Stat, Diepenbeek, Belgium. Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA.Mallinckrodt, CH, Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA.cmallinc@lilly.com | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1002/pst.124 | - |
dc.identifier.isi | 000224261600002 | - |
dc.identifier.url | https://www.academia.edu/8393357/Choice_of_the_primary_analysis_in_longitudinal_clinical_trials | - |
item.accessRights | Restricted Access | - |
item.contributor | Mallinckrodt, Craig H. | - |
item.contributor | Watkin, John G. | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | Carroll, Raymond J. | - |
item.fullcitation | Mallinckrodt, Craig H.; Watkin, John G.; MOLENBERGHS, Geert & Carroll, Raymond J. (2004) Choice of the primary analysis in longitudinal clinical trials. In: PHARMACEUTICAL STATISTICS, 3(3). p. 161-169. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2005 | - |
crisitem.journal.issn | 1539-1604 | - |
crisitem.journal.eissn | 1539-1612 | - |
Appears in Collections: | Research publications |
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mallinckrodt2004.pdf Restricted Access | Published version | 166.08 kB | Adobe PDF | View/Open Request a copy |
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