Systematic review of models used in economic analyses in moderate-to-severe asthma and COPD
Bibliographic record
Abstract
BACKGROUND: Respiratory diseases exert a substantial burden on society, with newer drugs increasingly adding to the burden. Economic models are often used, but seldom reviewed. PURPOSE: To summarize economic models used in economic analyses of drugs treating moderate-to-severe/very severe asthma or chronic obstructive pulmonary disease (COPD). METHODS: This study searched Medline and Embase from inception to the end of February 2015 for cost-effectiveness/utility analyses that examined at least one drug against placebo, another drug, or other standard therapy in asthma or COPD. Two reviewers independently searched and extracted data with differences adjudicated via consensus discussion. Data extracted included model used and its qualities, validation methods, treatments compared, disease severity, analytic perspective, time horizon, data collection (pro- or retrospective), input rates and sources, costs and sources, planned sensitivity analyses, criteria for cost-effectiveness, reported outcomes, and sponsor. RESULTS: This study analyzed 53 articles; 14 (25%) on asthma and 39 (75%) COPD. Markov models were commonly used for both asthma and COPD-related economic evaluations. Relatively few studies validated their model. For asthma-related studies, 10 examined inhaled corticosteroids and nine studied omalizumab. Placebo or standard therapy was the comparison in 11 studies and active drugs in the remainder. CONCLUSIONS: Few studies include validation of their models. Furthermore, controversy concerning some results was uncovered in this study, which needs to be avoided in the future.
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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 | 0.055 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".