Document Detail

Attention should be given to multiplicity issues in systematic reviews.
MedLine Citation:
PMID:  18687287     Owner:  NLM     Status:  MEDLINE    
OBJECTIVE: The objective of this paper is to describe the problem of multiple comparisons in systematic reviews and to provide some guidelines on how to deal with it in practice. STUDY DESIGN AND SETTING: We describe common reasons for multiplicity in systematic reviews, and present some examples. We provide guidance on how to deal with multiplicity when it is unavoidable. RESULTS: We identified six common reasons for multiplicity in systematic reviews: multiple outcomes, multiple groups, multiple time points, multiple effect measures, subgroup analyses, and multiple looks at accumulating data. The existing methods to deal with multiplicity in single trials can not always be applied in systematic reviews. CONCLUSION: There is no simple and completely satisfactory solution to the problem of multiple comparisons in systematic reviews. More research is required to develop multiple comparison procedures for use in systematic reviews. Authors and consumers of systematic reviews should give serious attention to multiplicity in systematic reviews when presenting, interpreting and using the results of these reports.
Ralf Bender; Catey Bunce; Mike Clarke; Simon Gates; Stefan Lange; Nathan L Pace; Kristian Thorlund
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Publication Detail:
Type:  Journal Article; Review    
Journal Detail:
Title:  Journal of clinical epidemiology     Volume:  61     ISSN:  0895-4356     ISO Abbreviation:  J Clin Epidemiol     Publication Date:  2008 Sep 
Date Detail:
Created Date:  2008-08-08     Completed Date:  2008-09-16     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8801383     Medline TA:  J Clin Epidemiol     Country:  England    
Other Details:
Languages:  eng     Pagination:  857-65     Citation Subset:  IM    
Institute for Quality and Efficiency in Health Care, Dillenburger Street 27, Cologne, Germany.
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MeSH Terms
Bias (Epidemiology)
Biomedical Research / standards*
Data Interpretation, Statistical
Guidelines as Topic / standards*
Meta-Analysis as Topic*
Review Literature as Topic*

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