Cost-effectiveness of precision molecular diagnostic tests for stage II colorectal cancer
Bibliographic record
Abstract
Background: In colorectal cancer, inappropriate use of adjuvant chemotherapies may lead to significant increases in healthcare costs and harms to patients. Genome-based interventions are being increasingly used in the stratification of patients according to their risk profiles. However, earlier cost-effectiveness analyses of precision molecular diagnostics have indicated a paucity of data on comparative health economic outcomes. Our aim was to compare the cost-effectiveness of marketed genomic tests used in the prognosis of stage II colorectal cancer patients. Methods: A Markov model was developed to compare the cost-effectiveness of treatment guided by any one of the following genomic tests: 12-gene assay or the 18-gene expression assay or the 482-gene signature or the Immunoscore assay in a hypothetical cohort of patients (n=1,000) with stage II colorectal cancer. Our study investigated outcomes in three health states: no recurrence, recurrence and death. This study was conducted from a societal perspective, and a 3% discount was applied to the costs and health outcomes. Sensitivity analyses were performed to assess the uncertainty of model parameters on the results. Results: The cost of the Immunoscore assay strategy in stage II colorectal cancer patients was estimated to be US $23,564 with a gain of 3.903 quality-adjusted life years (QALYs) as compared with the 12-gene assay strategy at US $24,545 and 3.903 QALYs; the 18-gene assay strategy at US $28,374 and 3.623 QALYs; and the 482-gene signature treatment strategy at US $33,315 with 3.704 QALYs. Sensitivity analyses indicated that incremental cost-effectiveness ratio (ICER) values were sensitive to costs of genomic tests and adjuvant chemotherapies; and utilities related to patients in the no-recurrence health state. Conclusions: Overall, the Immunoscore assay seems to be a dominant strategy at a threshold willingness-to-pay of $50,000 per QALY, but in the US other tests have been used for longer. Thus, the 12-gene assay may generate cost savings compared to the 18-gene expression assay. The findings of our study may provide useful information to policymakers regarding selection of the most appropriate genomic test, and resource allocation decisions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".