Challenges in Small Sample Size Cluster Trials for Medication Adherence: Insights From <scp>TAKE</scp> ‐ <scp>IT</scp> ‐ <scp>TOO</scp>
Bibliographic record
Abstract
Cluster randomized controlled trials (CRTs) are valuable for interventions involving the clinical care team but often require larger sample sizes due to within-cluster correlation and between-cluster variability. The TAKE-IT-TOO pilot study aimed to estimate the intraclass correlation (ICC) necessary for designing a full-scale CRT to improve medication adherence in pediatric kidney transplant recipients. We examined different approaches to summarizing electronically measured adherence and assessed how these choices impact ICC estimation and sample size requirements. In TAKE-IT-TOO, seven centers were randomized to either an adherence-promoting intervention or a healthy-living intervention. Medication adherence was tracked using electronic pillboxes over a 4-week run-in period followed by a 10-week intervention. Variance estimates were calculated using generalized linear mixed models (GLMM) and used to derive ICC values. We compared two methods of summarizing adherence data and estimated the number of clusters and cluster sizes needed to detect an odds ratio of 1.5 between intervention groups across a range of plausible ICC values. The analysis demonstrated that using repeated adherence measures within each participant offered advantages over relying on a single summary measure, improving statistical power. However, large standard errors around variance estimates made precise ICC estimation difficult. Despite this, we identified a plausible range of ICC values and corresponding sample size requirements for future trials. The TAKE-IT-TOO study underscores the challenges of conducting CRTs in small populations and highlights the value of repeated outcome measures for maximizing statistical efficiency in adherence research.
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How this classification was reachedexpand
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".