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Cost-Effectiveness of Nivolumab Plus Ipilimumab versus Chemotherapy for Previously Untreated Metastatic NSCLC Using Mixture-Cure Survival Analysis Based on CheckMate 227 5-Year Data

  • Alison D. Griffiths,
  • Robert O. Young,
  • Yong Yuan,
  • Mohammad A. Chaudhary,
  • Adam Lee,
  • Jason Gordon,
  • Philip McEwan

摘要

Objectives

This study assessed the cost-effectiveness of nivolumab plus ipilimumab (NIVO+IPI) versus platinum-doublet chemotherapy (chemo) in untreated metastatic non-small cell lung cancer (NSCLC) using mixture-cure modelling, an approach used to analyse immuno-oncology treatments due to their underlying methods depicting delayed but durable response in some patients.

Methods

A mixture-cure economic model was developed from a US third-party payer perspective to assess the lifetime costs and benefits of NIVO+IPI versus chemo using data from Part 1 of the phase III CheckMate 227 trial with 5 years of follow-up. The model consisted of four health states: progression-free without long-term response (non-LTR), progression-free with long-term response (LTR), post-progression, and death. The primary outcomes were the incremental cost per life-year (LY) and quality-adjusted life-year (QALY) gained for NIVO+IPI versus chemo over a patient’s lifetime time horizon. Model uncertainty was evaluated using one-way sensitivity analyses and probabilistic sensitivity analysis.

Results

NIVO+IPI treatment showed a significant improvement in overall survival versus chemo; mean gain 1.69 LYs and 1.42 QALYs. The majority of the 4.04 LYs accrued by NIVO+IPI were in the LTR state (2.27 years). The incremental cost of NIVO+IPI versus chemo was US$125,321, resulting in an incremental cost/QALY gained of US$88,219.

Conclusions

This study suggests NIVO+IPI may be a cost-effective first-line treatment when compared with chemo in a US setting given a threshold of US$150,000 per QALY. The cost-effectiveness analysis used a mixture-cure approach, which may offer a more appropriate modelling method in immuno-oncology given LTR, by more accurately capturing the potential treatment benefit.