Health Technology Assessment for Molecular Diagnostics: Practices, Challenges, and Recommendations from the Medical Devices and Diagnostics Special Interest Group
Bibliographic record
Abstract
AbstractBackgroundHealth technology assessments (HTAs) are increasingly used to inform coverage, access, and utilization of medical technologies including molecular diagnostics (MDx). Although MDx are used to screen patients and inform disease management and treatment decisions, there is no uniform approach to their evaluation by HTA organizations.ObjectivesThe International Society for Pharmacoeconomics and Outcomes Research Devices and Diagnostics Special Interest Group reviewed diagnostic-specific HTA programs and identified elements representing common and best practices.MethodsMDx-specific HTA programs in Europe, Australia, and North America were characterized by methodology, evaluation framework, and impact. Published MDx HTAs were reviewed, and five representative case studies of test evaluations were developed: United Kingdom (National Institute for Health and Care Excellence's Diagnostics Assessment Programme, epidermal growth factor receptor tyrosine kinase mutation), United States (Palmetto's Molecular Diagnostic Services Program, OncotypeDx prostate cancer test), Germany (Institute for Quality and Efficiency in Healthcare, human papillomavirus testing), Australia (Medical Services Advisory Committee, anaplastic lymphoma kinase testing for non–small cell lung cancer), and Canada (Canadian Agency for Drugs and Technologies in Health, Rapid Response: Non-invasive Prenatal Testing).ResultsOverall, the few HTA programs that have MDx-specific methods do not provide clear parameters of acceptability related to clinical and analytic performance, clinical utility, and economic impact. The case studies highlight similarities and differences in evaluation approaches across HTAs in the performance metrics used (analytic and clinical validity, clinical utility), evidence requirements, and how value is measured. Not all HTAs are directly linked to reimbursement outcomes.ConclusionsTo improve MDx HTAs, organizations should provide greater transparency, better communication and collaboration between industry and HTA stakeholders, clearer links between HTA and funding decisions, explicit recognition of and rationale for differential approaches to laboratory-developed versus regulatory-approved test, and clear evidence requirements.
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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 | 0.180 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.015 |
| 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; 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".