Reevaluation of statistically significant meta‐analyses in advanced cancer patients using the <scp>Hartung–Knapp</scp> method and prediction intervals—A methodological study
Bibliographic record
Abstract
Using the Hartung-Knapp method and 95% prediction intervals (PIs) in random-effects meta-analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta-analyses using the Hartung-Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July 2019. We included systematic reviews reporting a statistically significant meta-analysis of at least four randomized controlled trials in advanced cancer patients using either a fixed-effect or random-effects model. We investigated the impact of switching from fixed-effect to random-effects meta-analysis and of using the recommended Hartung-Knapp method in random-effects meta-analyses. Furthermore, we calculated 95% PIs for all included meta-analyses. We identified 6234 hits, of which 261 statistically significant meta-analyses were included. Our recalculations of these 261 meta-analyses produced statistically significant results in 132 of 138 fixed-effect and 114 of 123 random-effects meta-analyses. When switching to a random-effects model, 19 of 132 fixed-effect meta-analyses (14.4%) were no longer statistically significant. Using the Hartung-Knapp method in random-effects meta-analyses resulted in 34 of 114 nonsignificant meta-analyses (29.8%). In the full sample (N = 261), the null effect was included by the 95% PI in 195 (74.7%) and the opposite effect (e.g., hazard ratio 0.5, opposite effect 2) in 98 meta-analyses (37.5%). Using the Hartung-Knapp method and PIs substantially influenced the interpretation of many published, statistically significant meta-analyses. We strongly encourage researchers to check if using the Hartung-Knapp method and reporting 95% PIs is appropriate in random-effects meta-analyses.
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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 | 0.276 | 0.531 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.078 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".