Covariate adjustment in cluster randomised trials: a practical guide
Bibliographic record
Abstract
Covariate adjustment can offer several potential benefits in the analysis of cluster randomised trials. These benefits include increasing statistical precision (ie, narrowing width of confidence intervals), as well as potentially reducing any bias arising from differential identification and recruitment across arms or missing outcome data. This article outlines a guideline for how to choose covariates to include in a prespecified adjustment plan for such trials. Recommendations include adjusting for covariates that have been included in any restricted randomisation; and adjusting for a prespecified set of covariates thought to be prognostic of the outcome, differential recruitment, or outcome missingness. When the prevalence of missing covariate or outcome data are non-negligible, a missing data technique such as multiple imputation (allowing for clustering), cluster mean imputation, or the missing indicator method, is recommended. In a case study, the proposed prespecified analysis plan includes adjustment for minimisation variables as well as four covariates thought to be prognostic of the outcome and potentially related to unblinded identification of participants after randomisation.
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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 | 0.072 | 0.251 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.051 | 0.037 |
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 source (direct Gemma or distilled Codex), 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".