Substantial risks associated with few clusters in cluster randomized and stepped wedge designs
Bibliographic record
Abstract
Given the growing attention to quality improvement, comparative effectiveness research, and pragmatic trials embedded within learning health systems, the use of the cluster randomization design is bound to increase. The number of clusters available for randomization is often limited in such trials. Designs that incorporate pre-intervention measurements (e.g. cluster cross-over, repeated parallel arm, and stepped wedge designs) can substantially reduce the required numbers of clusters by decreasing between-cluster sources of variation. However, there are substantial risks associated with few clusters, including increased probability of chance imbalances and type I and type II error, limited perceived or actual generalizability, and fewer options for statistical analysis. Furthermore, current sample size methods for the stepped wedge design make a strong underlying assumption with respect to the correlation structure-in particular, that the intracluster and inter-period correlations are equal. This is in contrast with methods for the cluster cross-over design that explicitly allow for a smaller inter-period correlation. Failing to similarly allow for the inter-period correlation in the design of a stepped wedge trial may yield perilously low sample sizes. Further methodological and empirical work is required to inform sample size methods and guidance for the stepped wedge trial and to provide minimum thresholds for this design.
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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 | 0.461 | 0.632 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".