With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research,Sports Medicine

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With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research
Sports Medicine ( IF 9.3 ) Pub Date : 2022-10-08 , DOI: 10.1007/s40279-022-01766-0
Daniel Kadlec ₁ , Kristin L Sainani ₂ , Sophia Nimphius ₁

Affiliation

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.

中文翻译：

能力越大，责任越大：力量与体能研究中元分析和元回归的常见错误

背景和目标

元分析和元回归经常被高度引用，并可能影响实践。不幸的是，荟萃分析中的统计错误很普遍，并可能导致有缺陷的结论。本文的目的是回顾荟萃分析中的常见统计错误，并记录它们在强度和条件反射研究中被高度引用的荟萃分析中的频率。

方法

我们从力量和条件研究中发现了一个被高度引用的元回归中的五个错误：难以置信的异常值；由于混淆标准偏差和标准误差而导致的高估效应量；未能说明相关观察；未能考虑研究内差异；并关注组内而不是组间结果。然后，我们量化了过去 20 年力量和体能研究领域中 20 项被引用最多的荟萃分析中这些错误的频率。

结果

我们发现在力量和体能研究中引用最多的 20 个荟萃分析中有 85% 包含统计错误。几乎一半 (45%) 包含至少一个效应量，该效应量是使用标准误差而不是标准差错误计算的。在某些情况下，这会导致明显错误的效果大小，例如，效果大小为 11 或 14 个标准差。此外，45% 的人未能解释相关观察结果，尽管包括来自同一研究的许多效应量，并且通常来自同一研究中的同一组。