Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations.

Abstract

BackgroundStandard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments.ObjectiveThe aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments.MethodsStandard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation.ResultsA close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO.ConclusionsStandard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed.Electronic supplementary materialThe online version of this article (10.1007/s40273-019-00806-4) contains supplementary material, which is available to authorized users.