Performing a meta-analysis when study-level standard deviations are missing
Abstract Number:
3193
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Eric Leifer (1), Kathryn Flynn (2), Steven Keteyian (3), Dalane Kitzman (4), Vandana Sachdev (1)
Institutions:
(1) National Heart, Lung, and Blood Institute, N/A, (2) Medical College of Wisconsin, N/A, (3) Henry Ford Health, N/A, (4) Wake Forest University School of Medicine, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
People with heart failure with preserved ejection fraction often experience a reduced or poor quality of life (QOL). Regular exercise therapy potentially may improve their QOL. To date, 6 randomized clinical trials (RCTs) have evaluated this hypothesis; however, all 6 of them had small to moderate sample sizes. Thus, we conducted a meta-analysis of the 6 RCTs with relevant data. A meta-analysis requires each trial provide an estimated treatment effect and an estimated standard deviation (SD) for that treatment effect. Six of the RCTs provided a treatment effect estimate, but 4 of the RCTs did not provide a SD. We discuss how to impute an SD when it is missing. We also discuss when it is reasonable to use a fixed effects meta-analysis as opposed to a random effects meta-analysis and why a random effects meta-analysis may sometimes lead to a non-applicable conclusion. Finally, we make recommendations for reporting study-level data to improve future meta-analyses.
Keywords:
Meta-analysis|missing standard deviations|imputation|heart failure| |
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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