Considering Cluster Analysis in Sport Medicine and Injury Prevention Research
Bibliographic record
Abstract
OBJECTIVE: In the field of sport medicine and injury prevention in sport, prospective study designs implementing cluster randomization or grouping of subjects by cluster (ie, team, clinic, school, community) are becoming increasingly common. However, there are very few published studies in the field that adequately account for clustering effects in the design and analysis, leading to potentially spurious conclusions. This paper will review the implications of using a cluster RCT or other intervention or observational design grouping individuals by cluster and to highlight the practical implications of appropriate analysis considering the effects of clustering. DATA SOURCES/SYNTHESIS: Previously published papers have provided a foundation of expertise to discuss the often neglected impact of ignoring the effects of cluster in the design and analysis of cluster RCT and other study designs that group individuals by cluster in sport medicine. RESULTS: The loss of statistical efficiency inherent when a study design implements randomization or grouping by cluster is reviewed. Specifically, the effect of cluster design on sample size considerations and analysis are discussed in the context of data from a recently published cluster RCT examining the effectiveness of a balance training prevention strategy in youth basketball. CONCLUSIONS: Researchers in sport medicine are encouraged and challenged to consider appropriate research design and analytical techniques more consistently when study subjects function in the context of a cluster in order to avoid spurious results and misleading conclusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.132 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".