The Quality of Reporting in Published Cost-Utility Analyses, 1976–1997
Bibliographic record
Abstract
PURPOSE: Cost-utility analysis is a type of cost-effectiveness analysis in which health effects are measured in terms of quality-adjusted life-years (QALYs) gained. Such analyses have become popular for examining the health and economic consequences of health and medical interventions, and they have been recommended by leaders in the field. These recommendations emphasize the importance of good reporting practices. This study determined 1) the quality of reporting in published cost-utility analyses through 1997 and 2) whether reporting practices have improved over time. We examined quality of reporting by journal type and number of cost-utility analyses a journal has published. DATA SOURCES: Computerized databases were searched through 1997 for the Medical Subject Headings or text keywords quality-adjusted, QALY, and cost-utility analysis. Published bibliographies of the field were also searched. STUDY SELECTION: Original cost-utility analyses written in English were included. Cost-effectiveness analyses that measured health effects in units other than QALYs and review, editorial, or methodologic articles were excluded. DATA EXTRACTION: Each of the 228 articles found was audited independently by two trained readers who used a standard data collection form to determine the quality of reporting in several categories: disclosure of funding, framing, reporting of costs, reporting of preference weights, reporting of results, and discussion. RESULTS: The number of cost-utility analyses in the medical literature increased greatly between 1976 and 1997. Analyses covered a wide range of diseases and interventions. Most studies listed modeling assumptions (82%), described the comparator intervention (83%), reported sensitivity analysis (89%), and noted study limitations (84%). Only 52% clearly stated the study perspective; 34% did not disclose the funding source. Methods of reporting costs and preference weights varied widely. The quality of published analyses improved slightly over time and was higher in general clinical journals and in journals that published more of these analyses. CONCLUSIONS: The study results reveal an active and evolving field but also underscore the need for more consistency and clarity in reporting. Better peer review and independent, third-party audits may help in this regard. Future investigations should examine the quality of clinical and economic assumptions used in cost-utility analyses, in addition to whether analysts followed recommended protocols for performance and reporting.
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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 | 0.462 | 0.799 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.056 | 0.103 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".