Evidence‐based sample size estimation based upon an updated meta‐regression analysis
Bibliographic record
Abstract
A traditional meta-analysis examines the overall effectiveness of an intervention by producing a pooled estimate of treatment efficacy. In contrast to this, a meta-regression model seeks to determine whether a study-level covariate (X) is a plausible source of heterogeneity in a set of treatment effects. Upon performing such an analysis, the results may suggest the presence of a meaningful amount of variation in the treatment effects because of the covariate; however, the current set of trials may not provide sufficient statistical power for such a conclusion. The proposed approach provides quantitative insight into the amount of support that a new trial may provide to the hypothesis that X is a meaningful source of variation in an updated meta-regression model, which includes both the previously completed and the proposed trial. This empirical algorithm allows examination of the potential feasibility of a planned study of various sizes to further support or refute the hypothesis that X is a statistically significant source of variation. A detailed example illustrates the sample size estimation algorithm for both a planned individually or cluster randomized trial to investigate the now commonly accepted impact of geographical latitude on the observed effectiveness of the Bacillus Calmette-Guérin vaccine in the prevention of tuberculosis. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.190 | 0.416 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".