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.

Objectives: From 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.

Methods: A 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.

Results: In 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.

Conclusions: Tepotinib 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.