Missing data: Discussion points from the PSI missing data expert group |
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Authors: | Tomasz Burzykowski James Carpenter Corneel Coens Daniel Evans Lesley France Mike Kenward Peter Lane James Matcham David Morgan Alan Phillips James Roger Brian Sullivan Ian White Ly‐Mee Yu of the PSI Missing Data Expert Group |
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Institution: | 1. MSOURCE Medical Development, Warszawa, Poland;2. Medical Statistics Unit, London School of Hygiene & Tropical Medicine, London, UK;3. EORTC, European Organisation for Research and Treatment of Cancer, AISBL‐IVZW, Belgium, UK;4. Pfizer Sandwich Laboratories, Kent, UK;5. AstraZeneca, Parklands, Alderley Park, Macclesfield, Cheshire, UK;6. GlaxoSmithKline, Marlow, UK;7. Amgen Ltd, Cambridge, UK;8. Ipsen, Clinical Development Data Sciences, Berkshire, UK;9. ICON Clinical Research, Buckinghamshire, UK;10. GlaxoSmithKline Research and Development Ltd., Middlesex, UK;11. Statistical Solutions Ltd., Cork, Ireland;12. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK;13. Centre for Statistics in Medicine, University of Oxford, Oxford, UK |
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Abstract: | The 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. Copyright © 2009 John Wiley & Sons, Ltd. |
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Keywords: | missing data LOCF MMRM multiple imputation |
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