Meta-Analysis of Incidence Rate Data in the Presence of Zero-Event and Single-Arm Studies
Bibliographic record
Abstract
Unlike the classical two-stageDerSimonian and Laird meta-analysis method, the one-stage random-effectsPoisson and Negative-binomial models have the great advantage of including the information contained in studies reporting zero event in one or both arms and in studies with one missing arm. Since the Negative-binomial distribution relaxes the assumption of equi-dispersion made by the Poisson, it should perform better when data exhibit over-dispersion. However, the superiority of the Negative-binomial model with rare events and single-arm studies is unclear and needs to be investigated. Moreover, to the best of our knowledge, this model has never been investigatedin the context of a meta-analysis of incidence rate data with heterogeneous intervention effect. Therefore, we assessed the performance of the univariate and bivariate random-effects Poison and Negative-binomial models using simulations calibrated on a real dataset from a study onthe surgical management of phyllodes tumors. Results suggested that the bivariate random-effects Negative-binomial model should be favored for the meta-analysis of incidence rate data exhibiting over-dispersion, evenin the presence ofzero-event and single-arm studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.519 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".