Incorporating multiple interventions in meta-analysis: an evaluation of the mixed treatment comparison with the adjusted indirect comparison
Bibliographic record
Abstract
BACKGROUND: Comparing the effectiveness of interventions is now a requirement for regulatory approval in several countries. It also aids in clinical and public health decision-making. However, in the absence of head-to-head randomized trials (RCTs), determining the relative effectiveness of interventions is challenging. Several methodological options are now available. We aimed to determine the comparative validity of the adjusted indirect comparisons of RCTs with the mixed treatment comparison approach. METHODS: Using systematic searching, we identified all meta-analyses evaluating more than 3 interventions for a similar disease state with binary outcomes. We abstracted data on each clinical trial including population n and outcomes. We conducted fixed effects meta-analysis of each intervention versus mutual comparator and then applied the adjusted indirect comparison. We conducted a mixed treatment meta-analysis on all trials and compared the point estimates and 95% confidence/credible intervals (CIs/CrIs) to determine important differences. RESULTS: We included data from 7 reviews that met our inclusion criteria, allowing a total of 51 comparisons. According to the a priori consistency rule, we found 2 examples where the analytic comparisons were statistically significant using the mixed treatment comparison over the adjusted indirect comparisons and 1 example where this was vice versa. We found 6 examples where the direction of effect differed according to the indirect comparison method chosen and we found 9 examples where the confidence intervals were importantly different between approaches. CONCLUSION: In most analyses, the adjusted indirect comparison yields estimates of relative effectiveness equal to the mixed treatment comparison. In less complex indirect comparisons, where all studies share a mutual comparator, both approaches yield similar benefits. As comparisons become more complex, the mixed treatment comparison may be favoured.
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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 | 0.360 | 0.581 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.029 | 0.099 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".