Intracluster correlation coefficients for sample size calculations related to cardiovascular disease prevention and management in primary care practices
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Bibliographic record
Abstract
BACKGROUND: Few studies have comprehensively reported intracluster correlation coefficient (ICC) estimates for outcomes collected in primary care settings. Using data from a large primary care study, we aimed to: a) report ICCs for process-of-care and clinical outcome measures related to cardiovascular disease management and prevention, and b) investigate the impact of practice structure and rurality on ICC estimates. METHODS: We used baseline data from the Improved Delivery of Cardiovascular Care (IDOCC) trial to estimate ICC values. Data on 5,140 patients from 84 primary care practices across Eastern Ontario, Canada were collected through chart abstraction. ICC estimates were calculated using an ANOVA approach and were calculated for all patients and separately for patient subgroups defined by condition (i.e., coronary artery disease, diabetes, chronic kidney disease, hypertension, dyslipidemia, and smoking). We compared ICC estimates between practices in which data were collected from a single physician versus those that had multiple participating physicians and between urban versus rural practices. RESULTS: ICC estimates ranged from 0 to 0.173, with a median of 0.056. The median ICC estimate for dichotomous process outcomes (0.088) was higher than that for continuous clinical outcomes (0.035). ICC estimates calculated for single physician practices were higher than those for practices with multiple physicians for both process (average 3.9-times higher) and clinical measures (average 1.9-times higher). Urban practices tended to have higher process-of-care ICC estimates than rural practices, particularly for measuring lipid profiles and estimated glomerular filtration rates. CONCLUSION: To our knowledge, this is the most comprehensive summary of cardiovascular-related ICCs to be reported from Canadian primary care practices. Differences in ICC estimates based on practice structure and location highlight the importance of understanding the context in which external ICC estimates were determined prior to their use in sample size calculations. Failure to choose appropriate ICC estimates can have substantial implications for the design of a cluster randomized trial.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it