Abstract

Short clinical trial follow-up periods at the time of submission and heterogeneity in outcomes often introduce uncertainty in the health technology assessment of immuno-oncologic (IO) therapies. To address this, some agencies prefer to use conservative methods for survival extrapolation. To evaluate the predictive accuracy of various survival extrapolation methods using data from CheckMate 067, a phase III trial of nivolumab plus ipilimumab (NIVO+IPI) vs either monotherapy in untreated metastatic melanoma. Six different extrapolation models were independently fitted to overall survival (OS) data for the three arms of CheckMate 067 using successive database locks (DBL) at: 28, 40, 52 and 60 months. NICE DSU TSD 14 informed model selection. 60-month follow-up from CheckMate 067, as well as external data for ipilimumab, were used to evaluate the predictive accuracy of the survival models. Extrapolated 20-year mean survival times (20-YR MST) were also compared with a 5% discount rate applied. For the 28-month DBL, standard parametric models, piecewise models and splines underestimated 60-month survival (range -3.8% to -11.7% across methods/arms). Mixture cure models (MCM), parametric mixture models (PMM) and response-based landmark models (RBLM) provided more accurate estimates (range -3.2% to +2.0% across methods/arms). MCM, PMM and RBLM produced closer estimates of 7-year and 10-year OS when compared to the external data for ipilimumab than other methods. 20-YR MSTs were higher for MCM, PMM and RBLM (NIVO+IPI: 6.02-7.02 years) than those for other models (NIVO+IPI: 4.56-5.62 years). Estimates of 20-YR MST for MCM, PMM and RBLM were more consistent across the successive database locks than with other methods. MCM, PMM and RBLM provided accurate and consistent estimates of OS across a range of follow-up periods. Extrapolation models that fail to account for heterogeneity in survival may result in biased assessments of the survival benefit of IO therapies for melanoma.

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