title: Which is more generalizable, powerful and interpretable in meta-analyses, mean difference or standardized mean difference?
abstract: BACKGROUND
  To examine empirically whether the mean difference (MD) or the standardised mean difference (SMD) is more generalizable and statistically powerful in meta-analyses of continuous outcomes when the same unit is used.
  
  
  METHODS
  From all the Cochrane Database (March 2013), we identified systematic reviews that combined 3 or more randomised controlled trials (RCT) using the same continuous outcome. Generalizability was assessed using the I-squared (I2) and the percentage agreement. The percentage agreement was calculated by comparing the MD or SMD of each RCT with the corresponding MD or SMD from the meta-analysis of all the other RCTs. The statistical power was estimated using Z-scores. Meta-analyses were conducted using both random-effects and fixed-effect models.
  
  
  RESULTS
  1068 meta-analyses were included. The I2 index was significantly smaller for the SMD than for the MD (P < 0.0001, sign test). For continuous outcomes, the current Cochrane reviews pooled some extremely heterogeneous results. When all these or less heterogeneous subsets of the reviews were examined, the SMD always showed a greater percentage agreement than the MD. When the I2 index was less than 30%, the percentage agreement was 55.3% for MD and 59.8% for SMD in the random-effects model and 53.0% and 59.8%, respectively, in the fixed effect model (both P < 0.0001, sign test). Although the Z-scores were larger for MD than for SMD, there were no differences in the percentage of statistical significance between MD and SMD in either model.
  
  
  CONCLUSIONS
  The SMD was more generalizable than the MD. The MD had a greater statistical power than the SMD but did not result in material differences.
authors.affiliation: Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan. n.takeshima@gmail.com.
authors.forename: Nozomi
authors.initials: N
authors.lastname: Takeshima
authors.forename: Takashi
authors.initials: T
authors.lastname: Sozu
authors.forename: Aran
authors.initials: A
authors.lastname: Tajika
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Ogawa
authors.forename: Yu
authors.initials: Y
authors.lastname: Hayasaka
authors.forename: Toshiaki A
authors.initials: TA
authors.lastname: Furukawa
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*