Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Abstract

Background Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma. Methods Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate. Results Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts. Conclusion This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages. Highlights Given that short clinical trial follow-up periods and survival heterogeneity introduce uncertainty in the health technology assessment of oncology therapies, this study evaluated the suitability of conventional parametric survival modeling approaches as compared with more flexible models in the context of immune checkpoint inhibitors that have the potential to provide lasting survival benefits. This study used extended follow-up data from the phase III CheckMate 067 trial (NCT01844505) to assess the predictive accuracy of standard parametric models in comparison with more flexible methods for estimating the long-term survival benefit of the immune checkpoint inhibitor combination of nivolumab plus ipilimumab in advanced melanoma. Mixture cure, parametric mixture, and landmark response models provided more accurate estimates of long-term overall survival versus external data than other models tested. In this case study with immune checkpoint inhibitor therapies in advanced melanoma, extrapolation models that explicitly incorporate differences in cancer survival between observed or latent subgroups showed greater accuracy with both early and later data cuts than other approaches did.

Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067

“…Our study validated several peer studies and guidelines [ 4 , 6 – 15 , 18 ]. Firstly, models with the best GOF might not necessarily provide improved extrapolation performance.…”

“…Thirdly, external evidence can be helpful to validate the model choice, especially when dealing with immature data. Among previous studies, two have already compared the different extrapolation models through the case study of Checkmate 067 [ 4 , 18 ]. Gibson et al compared RCS with standard parametric models and found that RCS performed better in modeling PFS [ 4 ].…”

“…Note that generalized gamma has three parameters that can assume a variety of hazard shapes (e.g., unimodal, monotonically increasing or decreasing, or bathtub) [ 19 ]. Landmark models that require patients’ response status for choosing a landmark time point were not included in this study since we only had summary data instead of detailed IPD information [ 17 , 18 ]. For FP models, the best models for both first-order and second-order were included.…”

“…To improve the accuracy of extrapolating survival outcomes [ 17 , 18 ], we conducted the external validation. However, due to lack of data, the only external data found was from I-OS (overall survival data of ipilimumab), for which the longest follow-up time of 10 years has been reached.…”

“…The National Institute for Health and Care Excellence (NICE) has already published some guidelines regarding the process of fit and extrapolation [ 16 , 17 ]. Some current studies have also discussed different flexible modeling techniques in extrapolating survival outcomes regarding immunotherapies [ 7 , 18 – 22 ]. Through these studies, we can conclude that methods that provide more degrees of freedom may accurately represent survival for anti-cancer drugs, particularly if data are more mature or external data are available to inform the long-term extrapolations.…”