OBJECTIVE: Meta-analyses of continuous outcomes typically use mean differences (MDs) or standardized mean differences (SMDs) (MD in pooled standard deviation units). Ratio of means (RoM) is an alternative effect measure that performs comparably in simulation. We compared treatment effects and heterogeneity for RoM, MD, and SMD using empiric data. STUDY DESIGN AND SETTING: From the Cochrane Database (2008, issue 1), we included systematic reviews reporting continuous outcomes, selected the meta-analysis with the most (and ≥five) trials, and calculated MD (where possible), SMD, and RoM. For each pair of effect measures, we compared P-values separately for treatment effect and heterogeneity and assessed asymmetry of discordant pairs (statistically significant result for only one of two measures). RESULTS: Two hundred thirty-two of 5,053 reviews were included. Measures demonstrated similar treatment effects, with ≤6% discordant pairs and no asymmetry. A 0.5 SMD increase corresponded to 22 (95% confidence interval: 19, 24)% increase using RoM. There was less heterogeneity in RoM vs. MD (n=143, P=0.007), SMD vs. RoM (n=232, P=0.005), and SMD vs. MD (n=143, P=0.004). Comparing discordant pairs, fewer meta-analyses showed significant heterogeneity with SMD vs. RoM (P=0.04), consistent with the known bias of SMD. CONCLUSION: Empiric data from diverse meta-analyses demonstrate similar treatment effects and no large differences in heterogeneity of RoM compared with difference-based methods.
OBJECTIVE: Meta-analyses of continuous outcomes typically use mean differences (MDs) or standardized mean differences (SMDs) (MD in pooled standard deviation units). Ratio of means (RoM) is an alternative effect measure that performs comparably in simulation. We compared treatment effects and heterogeneity for RoM, MD, and SMD using empiric data. STUDY DESIGN AND SETTING: From the Cochrane Database (2008, issue 1), we included systematic reviews reporting continuous outcomes, selected the meta-analysis with the most (and ≥five) trials, and calculated MD (where possible), SMD, and RoM. For each pair of effect measures, we compared P-values separately for treatment effect and heterogeneity and assessed asymmetry of discordant pairs (statistically significant result for only one of two measures). RESULTS: Two hundred thirty-two of 5,053 reviews were included. Measures demonstrated similar treatment effects, with ≤6% discordant pairs and no asymmetry. A 0.5 SMD increase corresponded to 22 (95% confidence interval: 19, 24)% increase using RoM. There was less heterogeneity in RoM vs. MD (n=143, P=0.007), SMD vs. RoM (n=232, P=0.005), and SMD vs. MD (n=143, P=0.004). Comparing discordant pairs, fewer meta-analyses showed significant heterogeneity with SMD vs. RoM (P=0.04), consistent with the known bias of SMD. CONCLUSION: Empiric data from diverse meta-analyses demonstrate similar treatment effects and no large differences in heterogeneity of RoM compared with difference-based methods.
Authors: Bradley C Johnston; Pablo Alonso-Coello; Jan O Friedrich; Reem A Mustafa; Kari A O Tikkinen; Ignacio Neumann; Per O Vandvik; Elie A Akl; Bruno R da Costa; Neill K Adhikari; Gemma Mas Dalmau; Elise Kosunen; Jukka Mustonen; Mark W Crawford; Lehana Thabane; Gordon H Guyatt Journal: CMAJ Date: 2015-10-26 Impact factor: 8.262
Authors: Massimo Moretti; Maria Giuseppa Grollino; Sofia Pavanello; Roberta Bonfiglioli; Milena Villarini; Massimo Appolloni; Mariella Carrieri; Laura Sabatini; Luca Dominici; Laura Stronati; Giuseppe Mastrangelo; Anna Barbieri; Cristina Fatigoni; Giovanni Battista Bartolucci; Elisabetta Ceretti; Francesca Mussi; Silvano Monarca Journal: Int Arch Occup Environ Health Date: 2014-11-02 Impact factor: 3.015
Authors: Christian Brettschneider; Alexander Konnopka; Hannah König; Alexander Rommel; Julia Thom; Christian Schmidt; Hans-Helmut König Journal: Pharmacoeconomics Date: 2021-02-01 Impact factor: 4.981