An empirical comparison of three methods for multiple cutoff diagnostic test meta‐analysis of the Patient Health Questionnaire‐9 (<scp>PHQ</scp>‐9) depression screening tool using published data vs individual level data
Bibliographic record
Abstract
Selective cutoff reporting in primary diagnostic accuracy studies with continuous or ordinal data may result in biased estimates when meta-analyzing studies. Collecting individual participant data (IPD) and estimating accuracy across all relevant cutoffs for all studies can overcome such bias but is labour intensive. We meta-analyzed the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared results for two statistical methods proposed by Steinhauser and by Jones to account for missing cutoffs, with results from a series of bivariate random effects models (BRM) estimated separately at each cutoff. We applied the methods to a dataset that contained information only on cutoffs that were reported in the primary publications and to the full IPD dataset that contained information for all cutoffs for every study. For each method, we estimated pooled sensitivity and specificity and associated 95% confidence intervals for each cutoff and area under the curve (AUC). The full IPD dataset comprised data from 45 studies, 15 020 subjects, and 1972 cases of major depression and included information on every possible cutoff. When using data available in publications, using statistical approaches outperformed the BRM applied to the same data. AUC was similar for all approaches when using the full IPD dataset, though pooled estimates were slightly different. Overall, using statistical methods to fill in missing cutoff data recovered the receiver operating characteristic (ROC) curve from the full IPD dataset well when using only the published subset. All methods performed similarly when applied to the full IPD dataset.
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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 | 0.344 | 0.557 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.063 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".