Determinants of the intracluster correlation coefficient in cluster randomized trials: the case of implementation research
Bibliographic record
Abstract
The objective of this research was to identify determinants of the magnitude of intracluster correlation coefficients (ICCs) in cluster randomized trials from the field of implementation research. A survey of experts was conducted to generate a priori hypotheses of factors that might affect ICC size. Hypotheses were tested on empirical estimates of ICCs calculated from 21 implementation research datasets, mainly from the UK. Effects of setting (primary or secondary care), type of variable (process or outcome), type of measurement (objective or subjective), prevalence of outcome and size of cluster were tested. In total, 220 ICCs were available (range 0 to 0.415). Significant differences in ICC magnitude were found. The ICCs were significantly higher for process than for outcome variables, and for secondary care outcomes compared with primary care outcomes. The effects of prevalence and size were less clear cut. There was no evidence to suggest that type of measurement affected ICC size. In conclusion, accurate estimates of ICCs are essential for sample size calculations for cluster randomized trials of professional behaviour change interventions. This study demonstrates that ICCs are sensitive to a number of trial factors, particularly setting and outcome type. These factors must be considered when planning such cluster randomized trials.
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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 | 0.811 | 0.946 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.015 |
| 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; 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".