Early Cost-effectiveness Analysis of Risk-Based Selection Strategies for Adjuvant Treatment in Stage II Colon Cancer:
Gabrielle Jongeneel1, Marjolein J E Greuter2, Natalia Kunst2,3,4,5
1Department of Epidemiology and Data Science, Amsterdam UMC, VU University, Amsterdam, the Netherlands. g.jongeneel@amsterdamumc.nl.
Background:
To explore the potential value of consensus molecular subtypes (CMS) in stage II colon cancer treatment selection, we carried out an early cost-effectiveness assessment of a CMS-based strategy for adjuvant chemotherapy.
Methods:
We used a Markov cohort model to evaluate three selection strategies: (i) the Dutch guideline strategy (MSS+pT4), (ii) the mutation-based strategy (MSS plus a BRAF and/or KRAS mutation or MSS plus pT4), and (iii) the CMS-based strategy (CMS4 or pT4). Outcomes were number of colon cancer deaths per 1,000 patients, total discounted costs per patient (pp), and quality-adjusted life-years (QALY) pp. The analyses were conducted from a Dutch societal perspective. The robustness of model predictions was assessed in sensitivity analyses. To evaluate the value of future research, we performed a value of information (VOI) analysis.
Results:
The Dutch guideline strategy resulted in 8.10 QALYs pp and total costs of €23,660 pp. The CMS-based and mutation-based strategies were more effective and more costly, with 8.12 and 8.13 QALYs pp and €24,643 and €24,542 pp, respectively. Assuming a threshold of €50,000/QALY, the mutation-based strategy was considered as the optimal strategy in an incremental analysis. However, the VOI analysis showed substantial decision uncertainty driven by the molecular markers (expected value of partial perfect information: €18M).
Conclusions:
On the basis of current evidence, our analyses suggest that the mutation-based selection strategy would be the best use of resources. However, the extensive decision uncertainty for the molecular markers does not allow selection of an optimal strategy at present.
Impact:
Future research is needed to eliminate decision uncertainty driven by molecular markers.
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