Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations
Bibliographic record
Abstract
OBJECTIVES: To investigate the extent to which articles of economic evaluations of healthcare interventions indexed in MEDLINE incorporate research practices that promote transparency, openness, and reproducibility. STUDY DESIGN AND SETTING: We evaluated a random sample of health economic evaluations indexed in MEDLINE during 2019. We included articles written in English reporting an incremental cost-effectiveness ratio in terms of costs per life years gained, quality-adjusted life years, and/or disability-adjusted life years. Reproducible research practices, openness, and transparency in each article were extracted in duplicate. We explored whether reproducible research practices were associated with self-report use of a guideline. RESULTS: We included 200 studies published in 147 journals. Almost half were published as open access articles (n = 93; 47%). Most studies (n = 150; 75%) were model-based economic evaluations. In 109 (55%) studies, authors self-reported use a guideline (e.g., for study conduct or reporting). Few studies (n = 31; 16%) reported working from a protocol. In 112 (56%) studies, authors reported the data needed to recreate the incremental cost-effectiveness ratio for the base case analysis. This percentage was higher in studies using a guideline than studies not using a guideline (72/109 [66%] with guideline vs. 40/91 [44%] without guideline; risk ratio 1.50, 95% confidence interval 1.15-1.97). Only 10 (5%) studies mentioned access to raw data and analytic code for reanalyses. CONCLUSION: Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations. This study provides baseline data to compare future progress in the field.
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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 | 0.850 | 0.949 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".