Cost-Effectiveness of Tepotinib Versus Capmatinib for the Treatment of Adult Patients With Metastatic Non–Small Cell Lung Cancer Harboring Mesenchymal–Epithelial Transition Exon 14 Skipping

Abstract

ObjectivesFrom the US Medicare perspective, this study compared the cost-effectiveness of tepotinib and capmatinib for treating metastatic non–small cell lung cancer with tumors harboring mesenchymal–epithelial transition factor gene exon 14 skipping. MethodsA 3-state partitioned survival model assessed outcomes over a lifetime horizon. Parametric survival analysis of the phase 2 VISION trial informed clinical inputs for tepotinib. Capmatinib inputs were captured using hazard ratios derived from an unanchored matching-adjusted indirect comparison study and published literature. National cost databases, trial data, and literature furnished drug, treatment monitoring, and disease/adverse event management expenditures (2021 US dollars) and utility inputs. Outcomes were discounted at 3% annually. ResultsIn the base case, tepotinib dominated capmatinib in frontline settings (incremental discounted quality-adjusted life-years [QALYs] and costs of 0.2127 and −$47 756, respectively) while realizing an incremental cost-effectiveness ratio of $274 514/QALY in subsequent lines (incremental QALYs and costs of 0.3330 and $91 401, respectively). In a line agnostic context, tepotinib produced an incremental cost-effectiveness ratio of $105 383/QALY (incremental QALYs and costs of 0.2794 and $29 447, respectively). Sensitivity and scenarios analyses for individual lines typically supported the base case, whereas those for the line agnostic setting suggested sensitivity to drug acquisition costs and efficacy inputs. ConclusionsTepotinib could be cost-effective versus capmatinib in frontline and line agnostic contexts, considering the range of willingness-to-pay thresholds recommended by the Institute for Clinical and Economic Review ($100 000-$150 000/QALY). Tepotinib could be cost-effective in subsequent lines at higher willingness-to-pay levels. These results are to be interpreted cautiously, considering uncertainty in key model inputs.