Reproducible research practices, openness and transparency in health economic evaluations: study protocol for a cross-sectional comparative analysis
Bibliographic record
Abstract
INTRODUCTION: There has been a growing awareness of the need for rigorously and transparent reported health research, to ensure the reproducibility of studies by future researchers. Health economic evaluations, the comparative analysis of alternative interventions in terms of their costs and consequences, have been promoted as an important tool to inform decision-making. The objective of this study will be 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. METHODS AND ANALYSIS: This is the study protocol for a cross-sectional comparative analysis. We registered the study protocol within the Open Science Framework (osf.io/gzaxr). We will evaluate a random sample of 600 cost-effectiveness analysis publications, a specific form of health economic evaluations, indexed in MEDLINE during 2012 (n=200), 2019 (n=200) and 2022 (n=200). We will include published papers 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. Screening and selection of articles will be conducted by at least two researchers. Reproducible research practices, openness and transparency in each article will be extracted using a standardised data extraction form by multiple researchers, with a 33% random sample (n=200) extracted in duplicate. Information on general, methodological and reproducibility items will be reported, stratified by year, citation of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement and journal. Risk ratios with 95% CIs will be calculated to represent changes in reporting between 2012-2019 and 2019-2022. ETHICS AND DISSEMINATION: Due to the nature of the proposed study, no ethical approval will be required. All data will be deposited in a cross-disciplinary public repository. It is anticipated the study findings could be relevant to a variety of audiences. Study findings will be disseminated at scientific conferences and published in peer-reviewed journals.
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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 | 0.300 | 0.430 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".