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Which is more generalizable, powerful and interpretable in meta-analyses, mean difference or standardized mean

Nozomi Takeshima1, Takashi Sozu, Aran Tajika

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The standardized mean difference (SMD) is more generalizable than the mean difference (MD) in meta-analyses of continuous outcomes. While MD showed greater statistical power, the difference was not material, suggesting SMD is often preferable.

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Area of Science:

  • Biostatistics
  • Evidence-based medicine
  • Systematic reviews

Background:

  • Meta-analyses of continuous outcomes commonly use mean difference (MD) or standardized mean difference (SMD).
  • Empirical examination is needed to determine which metric is more generalizable and statistically powerful.

Purpose of the Study:

  • To compare the generalizability and statistical power of MD and SMD in meta-analyses.
  • To assess which metric is more suitable for continuous outcomes with consistent units.

Main Methods:

  • Systematic review of Cochrane Database (March 2013) meta-analyses with >=3 randomized controlled trials (RCTs) and a continuous outcome.
  • Generalizability assessed by I-squared (I2) and percentage agreement between individual RCT effect sizes and meta-analysis results.
  • Statistical power estimated using Z-scores; analyses conducted with random-effects and fixed-effect models.

Main Results:

  • 1068 meta-analyses were analyzed. I2 was significantly smaller for SMD than MD (P < 0.0001).
  • SMD consistently demonstrated higher percentage agreement than MD across varying heterogeneity levels.
  • Despite higher Z-scores for MD, no significant differences in statistical significance rates were observed between MD and SMD.

Conclusions:

  • SMD is more generalizable than MD in meta-analyses of continuous outcomes.
  • MD exhibited greater statistical power, but this difference was not clinically significant.
  • SMD is a robust alternative to MD, particularly when dealing with heterogeneous data.