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Related Experiment Videos

Imputing missing standard deviations in meta-analyses can provide accurate results.

Toshi A Furukawa1, Corrado Barbui, Andrea Cipriani

  • 1Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Mizuho-cho, Mizuho-ku, Nagoya 467-8601 Japan. furukawa@med.nagoya-cu.ac.jp

Journal of Clinical Epidemiology
|December 20, 2005
PubMed
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It is safe to use standard deviations (SDs) from other studies when analyzing randomized controlled trials (RCTs) that omit them. This imputation method accurately reflects actual results in meta-analyses, ensuring reliable findings for antidepressant studies.

Area of Science:

  • Psychiatry and Mental Health Research
  • Biostatistics and Meta-Analysis
  • Clinical Trial Methodology

Background:

  • Randomized controlled trials (RCTs) frequently omit standard deviations (SDs) for continuous outcomes, hindering meta-analysis.
  • Meta-analysts often impute missing SDs using data from other studies, but the validity of this practice is unexamined.

Purpose of the Study:

  • To empirically assess the validity of imputing standard deviations (SDs) from other studies in meta-analyses.
  • To compare actual standardized mean differences (SMDs) with imputed SMDs in antidepressant RCTs.

Main Methods:

  • Two meta-analyses of antidepressants (fluoxetine and amitriptyline) were analyzed.
  • Actual SMDs and pooled SMDs were compared against those derived from two imputation methods using reported means and SDs.

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  • Agreement was assessed using ANOVA intraclass correlation coefficients.

    • Main Results:

    • The study included 39 fluoxetine RCTs (n=3,681) and 25 amitriptyline RCTs (n=1,832).
    • Imputed SMDs for individual RCTs showed very good agreement with actual SMDs (intraclass correlation coefficients 0.61-0.97).
    • Agreement between actual and imputed pooled SMDs was even better, with minimal differences in point estimates and confidence intervals.

    Conclusions:

    • Imputing standard deviations (SDs) by borrowing from other studies is a safe and valid practice for systematic reviews.
    • This method provides reliable estimates for meta-analyses when individual trials lack reported SDs.