Bayesian meta-analysis of studies with rare events: Do the choice of prior distributions and the exclusion of studies without events in both arms matter?
Bibliographic record
Abstract
Randomized controlled trials (RCTs) analyzing serious adverse events often observe low incidence and might even observe zero events in either or both of the treatment and control arms. In the meta-analysis of RCTs of adverse events, it is unclear whether trials with zero events in both arms provide any information for the summary risk ratio (RR) or odds ratio (OR). Studies with zero events in both arms are usually excluded in both frequentist and Bayesian meta-analysis . We used a fully probabilistic approach—a Bayesian framework—for the meta-analysis of studies with rare events, and systematically assessed whether exclusion of studies with no events in both arms produced different results compared to keeping all studies in the meta-analysis. We did this by conducting a simulation study in which we assessed the bias in the point estimate of the log(OR) and the coverage of the 95% posterior interval for the log(OR) for different analytical decisions and choices in fixed effect and random effects meta-analysis. We used simulated data generated from a known fixed effect or random effects data scenario (each scenario with a 1000 meta-analysis data-set). We found that the uniform and Jeffrey’s prior on the baseline risk in the control group leads to biased results and a reduced coverage, and that setting the prior distribution on the log(odds) scale worked better. We also found nearly identical results regardless of whether studies with no events in both arms were excluded or not.
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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 | 0.399 | 0.672 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".