Interpreting discordant indirect and multiple treatment comparison meta-analyses: an evaluation of direct acting antivirals for chronic hepatitis C infection
Bibliographic record
Abstract
Indirect treatment comparison (ITC) and multiple treatment comparison (MTC) meta-analyses are increasingly being used to estimate the comparative effectiveness of interventions when head-to-head data do not exist. ITC meta-analyses can be conducted using simple methodology to compare two interventions. MTC meta-analyses can be conducted using more complex methodology, often employing Bayesian approaches, to compare multiple interventions. As the number of ITC and MTC meta-analyses increase, it is common to find multiple analyses evaluating the same interventions in similar therapeutic areas. Depending on the choice of the methodological approach, the conclusions about relative treatment efficacy may differ. Such situations create uncertainty for decision makers. An illustration of this is provided by four ITC and MTC meta-analyses assessing the efficacy of boceprevir and telaprevir for chronic hepatitis C virus infection. This paper examines why these evaluations provide discordant results by examining specific methodological issues that can strengthen or weaken inferences.
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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 | 0.305 | 0.607 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".