In search of the evidentiary foundation of published Canadian economic evaluations (2001–06)
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to present a review of economic evaluations conducted from a Canadian perspective and to characterize sources of evidence and statistical methods to analyze effectiveness measures, resource utilization, and uncertainty. METHODS: A search strategy was developed to identify Canadian economic evaluations published between January 2001 and June 2006. A standardized abstraction form was used to extract key data (e.g., study design, data sources, statistical methods). RESULTS: A total of 153 unique studies were included for review, of which 75 were evaluations of drug therapies and less than half were funded by industry. Cost-effectiveness analysis was the most common type of economic evaluation and 80 percent of the studies used modeling techniques. A single source of evidence for effectiveness measures was used in half of the studies. Statistical methods were commonly reported to compare effectiveness measures when the economic evaluation was conducted alongside a clinical trial but less commonly when determining effectiveness input parameters in model-based economic evaluations, or to analyze resource utilization data. Authors relied mostly on univariate sensitivity analyses to explore uncertainty. CONCLUSIONS: This review identifies the need to improve the conduct and reporting of statistical methods for economic evaluations to improve confidence in the results.
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 | 0.054 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.055 | 0.057 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".