Statistical methods for detecting outlying and influential studies in meta-analysis of diagnostic test accuracy studies
Bibliographic record
Abstract
Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these models may be distorted in the presence of outlying or influential studies. Currently, subjective methods such as inspection of forest plots are used to identify outlying studies in meta-analysis of diagnostic test accuracy studies. We proposed objective methods based on solid statistical reasoning for identifying outlying and/or influential studies. The proposed methods have been validated using simulation study and illustrated on two published meta-analysis data. Our methods outperform and neglect the subjectivity of the currently used ad hoc methods. The proposed methods can be used as a sensitivity analysis tool concurrently with the current bivariate random-effects models or as a preliminary analysis tool for robust models that accommodate outlying and/or influential studies in meta-analysis of diagnostic test accuracy 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 | 0.041 | 0.596 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".