Identification of Optimal Study Weights in Meta-Analyses with a Binary Outcome
Bibliographic record
Abstract
Meta-analysis is a method that combines the results of multiple studies, so that the overall treatment effect can be estimated. However, the traditional method of study weight estimation by taking the reciprocals of the estimated variances is biased. For binary outcome data from a clinical trial, the accuracy of estimation of single study weight, summary effect, and variance of summary effect from the developed bias correction factors for log relative risk (RD), log relative risk (lnRR) or log odds ratio (lnOR) were assessed. When sample sizes are small, zero cell frequencies often occur in contingency tables and make parameter estimation more difficult. Methods of dealing with zero-cells were elaborated, which including adding 0.5 to the zero cell, adding 0.5 to all cells in the table if a zero frequency occurs, adding 0.5 to all cells all the time, and adding the reciprocal of the size of the contrasting study arm to each cell when a zero frequency occurs. In addition, for risk difference, adding 0.5 to the zero cells when two zero cells occur, and adding 0.5 to all the cells when two zero cells occur are also considered since the continuity of the weight of risk difference is only affected by double zero frequencies. Impact of bias correction on real meta- analyses from Cochrane Database was demonstrated.
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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 | 0.206 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".