Economic evaluations conducted by Canadian health technology assessment agencies: Where do we stand?
Bibliographic record
Abstract
OBJECTIVES: To examine the production of Health Technology Assessments (HTAs) with economic evaluations (EEs) conducted by Canadian HTA agencies. METHODS: This research used a three-step approach: (i) the Web sites of five Canadian organizations promoting HTA were searched to identify HTA reports with EEs; (ii) HTA agencies were surveyed to verify that our information was complete with respect to HTA activities and to describe the factors that influence the HTA process in Canada (i.e., selection of HTA topics, execution, dissemination of results and future trends); (iii) HTAs with EEs were appraised in terms of study design, retrieval of clinical and economic evidence, resource utilization and costing, effectiveness measures, treatment of uncertainty as well as presence of a budget impact analysis (BIA), and policy recommendations. RESULTS: Two hundred forty-nine HTA reports were identified of which 19 percent included EEs (n = 48). Decision analytic techniques were used in approximately 75 percent of the forty-eight EEs and probabilistic sensitivity analyses were commonly used by all agencies to deal with parameter uncertainty. BIAs or policy recommendations were given in 50 percent of the evaluations. Differences between agencies were observed in terms of selection of topics, focus of assessment and production of HTA (e.g., in-house activities). Major barriers to the conduct of HTAs with EEs were capacity, a lack of interest by decision makers and a lack of robust clinical information. CONCLUSIONS: The results of this research point to the need for increased HTA training, collaboration, evidence synthesis, and use of pragmatic "real world" evaluations.
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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 | 0.449 | 0.793 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.037 | 0.060 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.037 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".