Guidelines for the content of statistical analysis plans in clinical trials: protocol for an extension to cluster randomized trials
Bibliographic record
Abstract
BACKGROUND: Guidance exists to inform the content of statistical analysis plans in clinical trials. Though not explicitly stated, this guidance is generally focused on clinical trials in which the randomization units are individual patients and not groups of patients. There are critical considerations for the analysis of cluster randomized trials, such as accounting for clustering, the risk of imbalances between the arms due to post-randomization recruitment, and the need to use small sample corrections when the number of clusters is small. METHODS: This paper outlines the protocol for the development of a set of reporting guidelines for the content of statistical analysis plans for cluster randomized trials (including variations such as the stepped wedge cluster randomized trial and other cluster cross-over designs) by extending the minimum reporting analysis requirements as previously defined for individually randomized trials to cluster randomized trials. The guideline will be developed using a consensus-based approach, modifying existing reporting items from the guideline for individually randomized trials and extending to include new items. DISCUSSION: The guideline will be developed so it can be used independently of the guideline for individually randomized designs. The consensus guidelines will be published in an open-access journal, including key guidance as well as exploration and elaboration.
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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 | 0.432 | 0.642 |
| Meta-epidemiology (narrow) | 0.006 | 0.012 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.024 | 0.037 |
| Insufficient payload (model declined to judge) | 0.052 | 0.050 |
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".