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Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data.
MedLine Citation:
PMID:  23303608     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We describe methods for meta-analysis of randomised trials where a continuous outcome is of interest, such as blood pressure, recorded at both baseline (pre treatment) and follow-up (post treatment). We used four examples for illustration, covering situations with and without individual participant data (IPD) and with and without baseline imbalance between treatment groups in each trial. Given IPD, meta-analysts can choose to synthesise treatment effect estimates derived using analysis of covariance (ANCOVA), a regression of just final scores, or a regression of the change scores. When there is baseline balance in each trial, treatment effect estimates derived using ANCOVA are more precise and thus preferred. However, we show that meta-analysis results for the summary treatment effect are similar regardless of the approach taken. Thus, without IPD, if trials are balanced, reviewers can happily utilise treatment effect estimates derived from any of the approaches. However, when some trials have baseline imbalance, meta-analysts should use treatment effect estimates derived from ANCOVA, as this adjusts for imbalance and accounts for the correlation between baseline and follow-up; we show that the other approaches can give substantially different meta-analysis results. Without IPD and with unavailable ANCOVA estimates, reviewers should limit meta-analyses to those trials with baseline balance. Trowman's method to adjust for baseline imbalance without IPD performs poorly in our examples and so is not recommended. Finally, we extend the ANCOVA model to estimate the interaction between treatment effect and baseline values and compare options for estimating this interaction given only aggregate data. Copyright © 2013 John Wiley & Sons, Ltd.
Authors:
Richard D Riley; Iram Kauser; Martin Bland; Lutgarde Thijs; Jan A Staessen; Jiguang Wang; Francois Gueyffier; Jonathan J Deeks
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-10
Journal Detail:
Title:  Statistics in medicine     Volume:  -     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2013 John Wiley & Sons, Ltd.
Affiliation:
School of Health and Population Sciences, University of Birmingham, Public Health Building, Edgbaston, Birmingham, B15 2TT, U.K.
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