A comparison between integrating clinical practice setting and randomized controlled trial setting into economic evaluation models of therapeutics
Bibliographic record
Abstract
BACKGROUND: Cost-effectiveness analyses generated from randomized controlled trials (RCTs) represent results obtained under ideal experimental conditions (efficacy) and the applicability of these data to real-world settings (effectiveness) may be questionable. OBJECTIVE: To compare cost-effectiveness results obtained from a RCT setting with the results derived from community-based clinical practice. METHODS: Using data from a community-based cohort study and from a RCT, two cost-effectiveness analyses were performed and the incremental cost-effectiveness ratios (ICERs) were calculated for the use of etanercept in the treatment of patients with rheumatoid arthritis. RESULTS: Using an effectiveness-based analysis, the mean quality-adjusted life years (QALYs) gained during the 12-month monitoring period were 0.45 and 0.35 for the treatment and control groups respectively. The ICER for etanercept treatment was 174,200 dollars (CDN) per QALY (95% confidence limits between 119,500 dollars and 285,000 dollars). Incorporating efficacy data obtained from the RCT into the analysis, the mean QALYs gained were 0.56 and 0.35 for the treatment and control groups respectively. This resulted in a substantially lower ICER for etanercept treatment of 82,952 dollars per QALY (95% confidence limits between 66,500 dollars and 103,430 dollars). CONCLUSION: Depending on the type of clinical setting used for the analysis, the resulting ICER for etanercept treatment was very different. These results help to explain the difference in cost-effectiveness reported in previous modeling studies, some based on RCT assumptions and some based on effectiveness setting.
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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 | 0.499 | 0.659 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".