Review of the quality of reporting of statistical analysis plans for cluster randomized trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The guideline for the content of Statistical Analysis Plans (SAPs) outlines recommendations for items to be included in SAPs. As yet there is no specific tailoring of this guideline for Cluster Randomized Trials (CRTs). There has also been no assessment of reporting quality of SAPs against this guideline. Our intention is to identify how well a sample of SAPs for CRTs are adhering to the reporting of key items in the current guidelines, as well as additional analysis aspects considered to be important in CRTs. METHODS: We include (i) fully published standalone SAPs identified via Ovid-MEDLINE and (ii) SAPs published as supplementary material or appendices to the final published report identified by searching an existing database of nearly 800 CRTs. RESULTS: The search identified 85 unique SAPs: 26 were published in standalone format and 59 were supplementary material to the full trial report. There was mixed clarity in reporting of items related to the current guideline (eg, most (61/85, 72%) reported what covariates will be included in any adjustment; but fewer (26/85, 31%) reported what method will be used to estimate the absolute measure of effect). Considering additional aspects important for CRTs, the majority (79/85, 93%) included a plan to allow for clustering in the analysis; but fewer (10/40, 25%) reported how a small number of clusters would be accommodated (this was only considered relevant for the subset of CRTs with fewer than 40 clusters). Few (5/85, 6%) reported how the intracluster correlation would be estimated. Few clearly reported statistical targets of inference: in only two SAPs (2/85, 2%) it was clear whether the objectives were related to the individual or cluster-level average; in trials where relevant, only three (3/70, 4%) clearly reported whether the objectives were related to the marginal or cluster-specific effect. CONCLUSION: This review has identified specific areas of poor quality of reporting that might need additional consideration when developing the guidance for the reporting of SAPs for CRTs.
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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 | 0.661 | 0.908 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.035 | 0.036 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".