Daniel Kadlec1, Kristin L Sainani2, Sophia Nimphius3. 1. School of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia. d.kadlec@ecu.edu.au. 2. Epidemiology and Population Health, Stanford University, Stanford, CA, USA. 3. School of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia.
Abstract
BACKGROUND AND OBJECTIVE: Meta-analysis and meta-regression are often highly cited and may influence practice. Unfortunately, statistical errors in meta-analyses are widespread and can lead to flawed conclusions. The purpose of this article was to review common statistical errors in meta-analyses and to document their frequency in highly cited meta-analyses from strength and conditioning research. METHODS: We identified five errors in one highly cited meta-regression from strength and conditioning research: implausible outliers; overestimated effect sizes that arise from confusing standard deviation with standard error; failure to account for correlated observations; failure to account for within-study variance; and a focus on within-group rather than between-group results. We then quantified the frequency of these errors in 20 of the most highly cited meta-analyses in the field of strength and conditioning research from the past 20 years. RESULTS: We found that 85% of the 20 most highly cited meta-analyses in strength and conditioning research contained statistical errors. Almost half (45%) contained at least one effect size that was mistakenly calculated using standard error rather than standard deviation. In several cases, this resulted in obviously wrong effect sizes, for example, effect sizes of 11 or 14 standard deviations. Additionally, 45% failed to account for correlated observations despite including numerous effect sizes from the same study and often from the same group within the same study. CONCLUSIONS: Statistical errors in meta-analysis and meta-regression are common in strength and conditioning research. We highlight five errors that authors, editors, and readers should check for when preparing or critically reviewing meta-analyses.
BACKGROUND AND OBJECTIVE: Meta-analysis and meta-regression are often highly cited and may influence practice. Unfortunately, statistical errors in meta-analyses are widespread and can lead to flawed conclusions. The purpose of this article was to review common statistical errors in meta-analyses and to document their frequency in highly cited meta-analyses from strength and conditioning research. METHODS: We identified five errors in one highly cited meta-regression from strength and conditioning research: implausible outliers; overestimated effect sizes that arise from confusing standard deviation with standard error; failure to account for correlated observations; failure to account for within-study variance; and a focus on within-group rather than between-group results. We then quantified the frequency of these errors in 20 of the most highly cited meta-analyses in the field of strength and conditioning research from the past 20 years. RESULTS: We found that 85% of the 20 most highly cited meta-analyses in strength and conditioning research contained statistical errors. Almost half (45%) contained at least one effect size that was mistakenly calculated using standard error rather than standard deviation. In several cases, this resulted in obviously wrong effect sizes, for example, effect sizes of 11 or 14 standard deviations. Additionally, 45% failed to account for correlated observations despite including numerous effect sizes from the same study and often from the same group within the same study. CONCLUSIONS: Statistical errors in meta-analysis and meta-regression are common in strength and conditioning research. We highlight five errors that authors, editors, and readers should check for when preparing or critically reviewing meta-analyses.
Authors: Goutham Rao; Francisco Lopez-Jimenez; Jack Boyd; Frank D'Amico; Nefertiti H Durant; Mark A Hlatky; George Howard; Katherine Kirley; Christopher Masi; Tiffany M Powell-Wiley; Anthony E Solomonides; Colin P West; Jennifer Wessel Journal: Circulation Date: 2017-08-07 Impact factor: 29.690
Authors: Carlos Balsalobre-Fernández; Carlos M Tejero-González; Juan Del Campo-Vecino; Dionisio Alonso-Curiel Journal: J Hum Kinet Date: 2013-03-28 Impact factor: 2.193