Economic Evaluations Conducted for Assessment of Genetic Testing Technologies: A Systematic Review
Bibliographic record
Abstract
AIMS: To systematically review the methods used in economic evaluations (EEs) included in health technology assessments (HTAs) of genetic testing technologies (GTTs). METHODS: A systematic search using bibliographic databases and gray literature was undertaken to identify HTA reports on GTTs that included EEs in addition to clinical effectiveness results. Studies were reviewed in terms of methodology and reporting. RESULTS: Of 361 identified citations, 15 HTAs consisting of 11 model-based and 4 trial-based EEs were included, more than 50% of which had moderate-to-low-quality scores mainly due to not reporting information on basic elements of a standard EE and inadequate management of uncertainty. Cost-effectiveness analysis accounted for 62% of studies. Approximately 66% of the studies adopted a third-party payer perspective, and 46% used a lifelong time horizon. The majority of studies exclusively included technical costs of testing (100%) and therapeutic or preventive interventions (60%). The most frequent variables tested in sensitivity analysis included costs (66%), effects (50%), and transition probabilities (58%). CONCLUSIONS: We found several methodological challenges in the reviewed EEs, including identification of a proper analytical perspective, inclusion of wider range of outcomes and costs, allowing for long-term medical and nonmedical impacts of genetic tests, and sufficient management of uncertainty. These issues should be carefully considered in future EEs of GTTs.
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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 | 0.092 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".