The cost-effectiveness of brigatinib in adult patients with ALK inhibitor–naive ALK-positive non–small cell lung cancer from a US perspective

BACKGROUND: The discovery of specific oncogenic drivers in non–small cell lung cancer (NSCLC) has led to the development of highly targeted anaplastic lymphoma kinase tyrosine kinase inhibitors (ALKis). Brigatinib is a next-generation ALKi associated with prolonged progression-free survival in patients with ALKi-naive ALK+ NSCLC. OBJECTIVE: To estimate the cost-effectiveness of brigatinib compared with crizotinib and alectinib in patients with ALKi-naive ALK+ NSCLC, from a US payer perspective. METHODS: A lifetime area under the curve–partitioned survival model with 4 health states was used to evaluate the relative cost-effectiveness of brigatinib in the ALKi-naive ALK+ NSCLC setting. Brigatinib was compared with crizotinib within a cost-effectiveness framework and compared with alectinib in a cost-comparison framework, where all efficacy outcomes were assumed equal. The efficacy of brigatinib and crizotinib was informed by the ALTA-1L trial, and an indirect treatment comparison was performed to inform the efficacy of brigatinib vs alectinib owing to a lack of head-to-head data. Costs were derived from public sources. The main outcomes of the model were total costs, quality-adjusted life-years (QALYs), life-years, and incremental cost-effectiveness ratios. Univariate and probabilistic sensitivity analyses, in addition to multiple scenario analyses, were conducted to assess the robustness of the model outcomes. RESULTS: The improved outcomes observed in ALTA-1L translated into QALY gains (+0.97) in the comparison of brigatinib vs crizotinib. The superior efficacy profile was associated with increased time on treatment with brigatinib, which drove the increase in costs vs crizotinib (+$210,519). The resulting base-case incremental cost-effectiveness ratio was $217,607/QALY gained. Compared with alectinib, brigatinib was associated with a cost difference of −$8,546. Sensitivity analysis suggested that extrapolation of overall survival, the assumptions relating to time on treatment, and subsequent therapy costs were the most influential determinants of results. Probabilistic sensitivity analysis suggested brigatinib had the highest probability of being cost-effective beyond willingness-to-pay thresholds of $236,000 per QALY vs crizotinib and alectinib. CONCLUSIONS: At list prices and under base-case assumptions in the current analysis, brigatinib was associated with cost-savings vs alectinib, and QALY gains but at higher costs vs crizotinib. Additional research into the real-world efficacy of ALKis is warranted to further understand the comparative cost-effectiveness of these therapies.