Intracluster correlation coefficients in osteoarthritis cluster randomized trials: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The design, analysis, and interpretation of cluster randomized clinical trials (RCTs) require accounting for potential correlation of observations on individuals within the same cluster. Reporting of observed intracluster correlation coefficients (ICCs) in cluster RCTs, as recommended by Consolidated Standards of Reporting Trials (CONSORT), facilitates sample size calculation of future cluster RCTs and understanding of the trial statistical power. Our objective was to summarize observed ICCs in osteoarthritis (OA) cluster RCTs. DESIGN: Systematic review of knee/hip OA cluster RCTs. We searched Cochrane Central Register of Controlled Trials for trials published from 2012, when CONSORT cluster RCTs extension was published, to September 2022. We calculated the proportion of cluster RCTs that reported observed ICCs. Of those that did, we extracted observed ICCs. PROSPERO: CRD42022365660. RESULTS: We screened 1121 references and included 20 cluster RCTs. Only 5 trials (25%) reported the observed ICC for at least one outcome variable. ICC values for pain outcomes were: 0, 0.01, 0.18; for physical function outcomes were: 0, 0.06, 0.13 (knee)/0.27 (hip); Western Ontario and McMaster Universities Arthritis Index (WOMAC) total: 0.02, 0.02; symptoms of anxiety/depression: 0.22; disability: 0; and global change: 0. One out of four (25%) trials reported an ICC that was larger than the ICC used for sample size calculation and thus was underpowered. CONCLUSIONS: Despite CONSORT statement recommendations for reporting cluster RCTs, few OA trials reported the observed ICC. Given the importance of the ICC to interpretation of trial results and future trial design, this reporting gap warrants attention.
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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 | 0.211 | 0.575 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.025 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".