Abstract
Nivolumab demonstrated significant recurrence-free survival (RFS) gains vs. ipilimumab in the phase 3 trial CheckMate-238, whilst the phase 3 trial CA184-029 showed superior RFS gains for ipilimumab vs. placebo. Modelling was used to estimate overall survival (OS) associated with nivolumab compared to observation (no treatment) when OS data was not available in CheckMate-238. Four modelling options were developed using different methods and evidence sources to estimate post-recurrence survival (PRS) and OS. For all options, the RFS parametric survival curves for nivolumab and observation were informed by a patient-level data meta-regression of the two trials. The first option applied a partitioned survival model (PSM), where the parametric OS curve for observation was derived from the CA184-029 placebo arm, and nivolumab OS was based on a surrogacy relationship between RFS and OS specific to adjuvant melanoma. The other options used state-transition Markov models to estimate PRS from different data sources: (1) assumed PRS is identical to OS curves from the PSM, applying a PRS vs OS hazard ratio from CA184-029; (2) used weighted survival of local/regional recurrence data in CA184-029 and a network meta-analysis of advanced melanoma treatments and (3) used PRS from CA184-029. In all options, long-term melanoma registry data were applied to both treatment arms at 10 years. The PSM estimated 21.1 and 13.9 life-years (LYs) for nivolumab and observation, respectively over a life time horizon. Markov option 1 reported 18.5 and 11.1 LYs, option 2 reported 13.9 and 9.1 LYs, and option 3 reported 17.0 and 12.1 LYs for nivolumab and observation, respectively. Modelling can be used to estimate OS for nivolumab and observation. Different modelling methods and evidence sources all predicted superior OS for nivolumab vs. observation, but with varying LY differences. Longer term OS data are required to validate these modelling options.
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