Statistical Power in Musculoskeletal Research: A Meta-Review of 266 Randomised Controlled Trials

Underpowered study designs undermine the reliability of experimental research, with growing concerns regarding randomised controlled trials (RCTs) informing musculoskeletal injury management. We assessed the statistical power and sample size calculations of such RCTs.

Electronic searches (MEDLINE and PEDro searched up to March 2024) identified meta-analyses of RCTs comparing conservative interventions for musculoskeletal injury, without restrictions on demographics, injury type, or outcome. Statistical power was estimated using two approaches: (1) meta-analytic-the RCT's power to detect the summary effect of the meta-analysis it contributed to, and (2) conventional-the RCT's power to detect Cohen's small (d = 0.2), medium (d = 0.5), and large (d = 0.8) effect sizes. The RCTs' manuscripts and registry entries were screened for sample size planning details.

The search identified 4737 articles, with 41 eligible meta-analyses of 266 RCTs. The median power was 42% (54% among RCTs within statistically significant meta-analyses). Less than 1 in 3 RCTs from statistically significant meta-analyses had ≥ 80% power to detect the corresponding summary effect. The number of RCTs with ≥ 80% power to detect small, medium, and large effects was 0%, 7.9%, and 37.6%, respectively. One in four RCTs reported sample size calculations; 80% expected larger effects than they observed. RCTs not reporting sample size calculations were smaller and reported larger effects.

Low statistical power permeates musculoskeletal injury research, limiting the clinical utility of many RCTs. The underlying causes of low power in this field are multifactorial and extend beyond sample size calculation alone. Enhancing study power requires methodological improvements, including robust planning, stronger theoretical frameworks, multi-center collaboration, data sharing, and the use of valid, reliable outcome measures.