The impact of incorporating Bayesian network meta-analysis in cost-effectiveness analysis - a case study of pharmacotherapies for moderate to severe COPD
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of using network meta-analysis (NMA) versus pair wise meta-analyses (PMA) for evidence synthesis on key outputs of cost-effectiveness analysis (CEA). METHODS: We conducted Bayesian NMA of randomized clinical trials providing head-to-head and placebo comparisons of the effect of pharmacotherapies on the exacerbation rate in chronic obstructive pulmonary disease (COPD). Separately, the subset of placebo-comparison trials was used in a Bayesian PMA. The pooled rate ratios (RR) were used to populate a decision-analytic model of COPD treatment to predict 10-year outcomes. RESULTS: Efficacy estimates from the NMA and PMA were similar, but the NMA provided estimates with higher precision. This resulted in similar incremental cost-effectiveness ratios (ICER). Probabilities of being cost-effective at willingness-to-pay thresholds (WTPs) between $25,000 and $100,000 per quality adjusted life year (QALY) varied considerably between the PMA- and NMA-based approaches. The largest difference in the probabilities of being cost-effective was observed at a WTP of approximately $40,000/QALY. At this threshold, with the PMA-based analysis, ICS, LAMA and placebo had a 43%, 30, and 18% probability of being the most cost-effective. By contrast, with the NMA based approach, ICS, LAMA, and placebo had a 56%, 19%, and 21% probability of being cost-effective. For larger WTP thresholds the probability of LAMA being the most cost-effective became higher than that of ICS. Under the PMA-based analyses the cross-over occurred at a WTP threshold between $60,000/QALY-$65,000/QALY, whereas under the NMA-based approach, the cross-over occurred between $85,000/QALY-$90,000/QALY. CONCLUSION: Use of NMAs in CEAs is feasible and, as our case study showed, can decrease uncertainty around key cost-effectiveness measures compared with the use of PMAs. The approval process of health technologies in many jurisdictions requires estimates of comparative efficacy and cost-effectiveness. NMAs play an increasingly important role in providing estimates of comparative efficacy. Their use in the CEAs therefore results in methodological consistency and reduced uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.210 | 0.448 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".