Using Cost-Effectiveness Analysis to Quantify the Value of Genomic-Based Diagnostic Tests: Recommendations for Practice and Research
Bibliographic record
Abstract
AIMS: New sequencing technologies allow increased opportunities to use genomic-based diagnostic tests (genomic tests) in routine clinical practice, which will impact healthcare budgets and patients' outcomes. This article aims to generate a list of recommendations on how the principles and methods of cost-effectiveness analysis (CEA) can be used to quantify the costs and benefits of genomic tests. METHODS: A systematic literature search identified publications describing the use of CEA to evaluate genomic tests. Data were extracted as key concepts to produce a thematic list of previously described challenges and solutions to using CEA to evaluate genomic tests. Defining features of evaluating genomic tests were categorized into a list of key recommendations for applying methods in practice and for research needs. RESULTS: Features producing challenges in the implementation of CEA to evaluate genomic tests were as follows: the ability of the tests to diagnose multiple disorders; potential consequences for future generations suggesting an infinite time horizon; and the potential need to consider nonhealth benefits. CONCLUSIONS: CEA was identified as an appropriate evaluative framework for genomic tests, although standard methods may need modification and important method research questions remain. Key recommendations suggest a need for research to reflect: sharing genomic information across generations; genomic tests for multiple disorders; and health and nonhealth benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".