Updated Tests for Small-study Effects in Meta-analyses
Bibliographic record
Abstract
This article describes an updated version of the metabias command, which provides statistical tests for funnel plot asymmetry. In addition to the previously implemented tests, metabias implements two new tests that are recommended in the recently updated Cochrane Handbook for Systematic Reviews of Interventions (Higgins and Green 2008). The first new test, proposed by Harbord, Egger, and Sterne (2006, Statistics in Medicine 25: 3443–3457), is a modified version of the commonly used test proposed by Egger et al. (1997, British Medical Journal 315: 629–634). It regresses Z/[Formula: see text] against [Formula: see text], where Z is the efficient score and V is Fisher's information (the variance of Z under the null hypothesis). The second new test is Peters’ test, which is based on a weighted linear regression of the intervention effect estimate on the reciprocal of the sample size. Both of these tests maintain better control of the false-positive rate than the test proposed by Egger at al., while retaining similar power.
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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 | 0.092 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".