Transferability of health technology assessments and economic evaluations: a systematic review of approaches for assessment and application
Bibliographic record
Abstract
Ron Goeree1,2, Jing He1,2, Daria O'Reilly1,2, Jean-Eric Tarride1,2, Feng Xie1,2, Morgan Lim1,2, Natasha Burke1,21Programs for Assessment of Technology in Health (PATH) Research Institute, St Joseph's Healthcare Hamilton, ON, Canada; 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, CanadaBackground: Health technology assessments (HTA) generally, and economic evaluations (EE) more specifically, have become an integral part of health care decision making around the world. However, these assessments are time consuming and expensive to conduct. Evaluation resources are scarce and therefore priorities need to be set for these assessments and the ability to use information from one country or region in another (geographic transferability) is an increasingly important consideration.Objectives: To review the existing approaches, systems, and tools for assessing the geographic transferability potential or guiding the conduct of transferring HTAs and EEs.Methods: A systematic literature review was conducted of several databases, supplemented with web searching, hand searching of journals, and bibliographic searching of identified articles. Systems, tools, checklists, and flow charts to assess, evaluate, or guide the conduct of transferability of HTAs and EEs were identified.Results: Of 282 references identified, 27 articles were reviewed in full text and of these, seven proposed unique systems, tools, checklists, or flow charts specifically for geographic transferability. All of the seven articles identified a checklist of transferability factors to consider, and most articles identified a subset of 'critical' factors for assessing transferability potential. Most of these critical factors related to study quality, transparency of methods, the level of reporting of methods and results, and the applicability of the treatment comparators to the target country. Some authors proposed a sequenced flow chart type approach, while others proposed an assessment of critical criteria first, followed by an assessment of other noncritical factors. Finally some authors proposed a quantitative score or index to measure transferability potential.Conclusion: Despite a number of publications on the topic, the proposed approaches and the factors used for assessing geographic transferability potential have varied substantially across the papers reviewed. Most promising is the identification of an extensive checklist of critical and noncritical factors in determining transferability potential, which may form the basis for consensus of a future tool. Due to the complexities of identifying appropriate weights for each of the noncritical factors, it is still uncertain whether the assessment and calculation of an overall transferability score or index will be practical or useful for transferability considerations in the future.Keywords: costs and cost analysis, economic evaluation, health technology assessment, geographic transferability, portability, generalizability
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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 | 0.317 | 0.658 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.044 | 0.042 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".