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Background Nivolumab (an anti–programmed death-1 antibody) is an adjuvant standard of care for patients with high-risk resected melanoma, although a watch-and-wait strategy remains an option. In the absence of head-to-head evidence, an indirect treatment comparison (ITC) of adjuvant nivolumab versus placebo, the proxy for a watch-and-wait strategy, was conducted in patients with high-risk resected melanoma. Methods An ITC using the Bucher method compared nivolumab with placebo using intention-to-treat population data from the phase III CheckMate 238 (nivolumab vs ipilimumab; minimum follow-up, 4 years; NCT02388906) and European Organisation for Research and Treatment of Cancer (EORTC) 18071 (ipilimumab vs placebo; minimum follow-up, ≈4.5 years; NCT00636168) trials. The end-points were recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and overall survival (OS). To account for cross-trial differences in staging and subsequent therapy, additional analyses examined patients with stage IIIB/IIIC disease and adjusted post-recurrence survival in EORTC 18071, respectively. Results Nivolumab versus placebo was associated with clinically meaningful improvements in RFS (hazard ratio [HR], 0.53; 95% confidence interval [CI], 0.42–0.68) and OS (HR, 0.63; 95% CI, 0.45–0.89). Nivolumab versus placebo was also associated with clinically meaningful improvements in RFS (HR, 0.53; 95% CI, 0.40–0.69), DMFS (HR, 0.62; 95% CI, 0.46–0.83) and OS (HR, 0.67; 95% CI, 0.47–0.97) in patients with stage IIIB/IIIC disease and in OS (HR, 0.65; 95% CI, 0.46–0.92) in the overall population after adjusting post-recurrence survival in EORTC 18071. Conclusion This ITC shows that adjuvant nivolumab provides clinically meaningful improvements in RFS, DMFS and OS versus a watch-and-wait strategy in high-risk resected melanoma.

Introduction The aim of the current study is to estimate the cost-effectiveness of adjuvant treatment with nivolumab relative to clinically relevant comparators in adult patients with melanoma with lymph node involvement or metastatic disease who have undergone complete resection from a French societal perspective. Methods The comparators were observation, low-dose interferon and pembrolizumab. A subgroup analysis was carried out in patients with BRAF mutation, adding dabrafenib plus trametinib. A three-state partitioned survival model was developed to project costs and health benefits over a 20-year time horizon. Extrapolation for recurrence-free survival (RFS) and overall survival (OS) was carried out using spline-based models. Because of the immaturity of OS data in pivotal trials for nivolumab and pembrolizumab, a predictive model of OS treatment effect based on RFS effect was developed using a correlation equation. Health state utilities and adverse events disutilities were derived from the CheckMate 238 trial and literature. Costs were estimated in 2019 euros. The model’s primary outcome was efficiency frontier. Deterministic and probabilistic sensitivity analyses were conducted to assess the robustness of results. Results Observation, low-dose interferon and nivolumab were on the efficiency frontier. The incremental cost–utility ratio of nivolumab versus low-dose interferon (closest therapy on the efficiency frontier) was €37,886/quality-adjusted life year (QALY). Probabilistic sensitivity analysis reported an 80% probability of nivolumab being a cost-effective strategy for a willingness-to-pay threshold of €52,000/QALY. In the subgroup with BRAF mutation, the efficiency frontier was not changed by the addition of dabrafenib plus trametinib. Conclusions Nivolumab is a cost-effective strategy as adjuvant treatment in adult patients with surgically resected melanoma in France. Electronic supplementary material The online version of this article (10.1007/s13555-020-00446-z) contains supplementary material, which is available to authorized users.

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.

Introduction Nivolumab demonstrated significant recurrence-free survival (RFS) gains versus ipilimumab in the CheckMate-238 trial, whereas the CA184-029 trial showed superior RFS gains for ipilimumab versus placebo. No head-to-head trial data were available to compare the efficacy of nivolumab to that of observation, so indirect treatment comparisons were required. Additionally, overall survival (OS) data were not available from CheckMate-238, and the clinical pathway for melanoma has changed significantly over the last decade. Four modelling options were developed using different methods and evidence sources to estimate OS and the impact of nivolumab on predicted life-years in the adjuvant setting; however, this article focuses on two primary methods. Methods RFS for nivolumab and observation were informed by a patient-level data meta-regression. The first model was a partitioned survival model, where the parametric OS curve for observation was derived from CA184-029 and nivolumab OS was based on a surrogacy relationship between RFS and OS specific to adjuvant melanoma. The other option used a state-transition model to estimate post-recurrence survival using different data sources. Results The modelling options estimated different OS for both nivolumab and observation but demonstrated at least a 32% increase in life-years gained for nivolumab versus observation. Conclusion This analysis demonstrated the difficulties in modelling within the adjuvant setting. Each model produced different survival projections, showing the need to explore different techniques to address the extent of uncertainty. This also highlighted the importance of understanding the impact of RFS in the long term in a setting where the aim of treatment is to remain disease free. Electronic supplementary material The online version of this article (10.1007/s41669-019-00181-y) contains supplementary material, which is available to authorized users.

Additional file 3: Appendix C. Results of the stage III/IV subgroup analyses. Table C.1. Stage III/IV population – constant hazard ratios estimated from fixed-effects network meta-analysis for recurrence-free/disease-free survival. Table C.2. Stage III/IV population – constant hazard ratios estimated from fixed-effects network meta-analysis for distant metastasis-free survival

Additional file 1: Appendix A. Systematic literature review details and results. Figure A.1. Study selection flow diagram of updated SLR (May 2019). Table A.1. Study selection criteria to identify trials for the systematic literature review. Table A.2. Search strategy for EMBASE (EMBASE 1974 to October 11, 2017; Search executed: October 12, 2017). Table A.3. Search strategy for MEDLINE (Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R) 1946 to Present; Search executed: October 12, 2017). Table A.4. Search strategy for Cochrane Register of Controlled Trials (Cochrane Register of Controlled Trials September 2017; Search executed: October 12, 2017). Table A.5. Search strategy for EMBASE (EMBASE 1974 to 2018 May 07; Search executed: May 8, 2018). Table A.6. Search strategy for MEDLINE (Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R) 1946 to Present; Search executed: May 8, 2018). Table A.7. Search strategy for Cochrane Register of Controlled Trials (Cochrane Register of Controlled Trials March 2018; Search executed: May 8, 2018). Table A.8. Search strategy for EMBASE (EMBASE 1974 to 2019 April 30; Search executed: May 1, 2019). Table A.9. Search strategy for MEDLINE (Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R) 1946 to Present; Search executed: May 1, 2019). Table A.10. Search strategy for Cochrane Register of Controlled Trials (Cochrane Register of Controlled Trials April 2019; Search executed: May 1, 2019). Table A.11. Patient characteristics in randomized controlled trials included in network meta-analyses. Table A.12. Cochrane risk of bias assessment of randomized controlled trials included in the feasibility assessment. Table A.13. Safety data from randomized controlled trials included in the feasibility assessment

Additional file 4: Appendix D. Safety analyses. Figure D.1. Network diagrams of randomized controlled trials for subgroup. Table D.1. Stage II/III/IV population – odds ratios estimated from fixed-effects network meta-analysis for grade 3/4 adverse events analysis of safety outcomes. Table D.2. Stage II/III/IV population – odds ratios estimated from fixed-effects network meta-analysis for discontinuations due to adverse events. Table D.3. Stage II/III/IV population – odds ratios estimated from fixed-effects network meta-analysis for discontinuations

Background We have previously performed indirect treatment comparisons (ITCs) to demonstrate improvements in recurrence-free survival (RFS) and distant metastasis-free survival with nivolumab versus placebo as adjuvant treatment for resected melanoma; however, overall survival (OS) data were not available at the time. Recently, results of the phase 3 CheckMate 238 trial in patients with resected stage IIIB–IIIC/IV melanoma (American Joint Committee on Cancer [AJCC], 7th edition) showed no statistically significant difference in OS between nivolumab and ipilimumab; however, OS events were fewer than expected. In the phase 3 EORTC 18071 trial in patients with resected stage IIIA–IIIC melanoma (AJCC, 6th edition), OS was improved with ipilimumab versus placebo. Here, we provide an update on RFS and an analysis of OS in ITCs of nivolumab and placebo using data from these 2 trials with a common comparator arm: ipilimumab 10 mg/kg. Methods ITCs of nivolumab versus placebo were conducted using 4-year minimum follow-up data from CheckMate 238 (NCT02388906) and 5.3-year median follow-up data from EORTC 18071 (NCT00636168). Bucher ITCs were performed to estimate RFS and OS in the intention-to-treat populations. A sensitivity analysis of OS adjusting for subsequent therapy options was conducted in 2 steps: (1) after controlling for possible confounders and assuming that the only difference was the effect of different subsequent therapies, postrecurrence survival was compared between the 2 ipilimumab arms in each study, and (2) after adjusting for differences in postrecurrence survival, ITCs of nivolumab versus adjusted placebo were performed. Results In these ITC analyses, RFS and OS results with nivolumab suggested an improvement compared with placebo. In the intention-to-treat population, nivolumab was associated with a lower risk of recurrence or death (RFS HR, 0.55; 95% CI, 0.43–0.70) and a lower risk of death (OS HR, 0.62; 95% CI, 0.44–0.88) than placebo. In the sensitivity analysis, a 63% average increase in postrecurrence survival benefit was estimated with ipilimumab in CheckMate 238 compared with ipilimumab in EORTC 18071. After adjusting for this increase in both the ipilimumab and placebo arms in EORTC 18071, nivolumab was associated with a lower risk of death than placebo (OS HR, 0.65; 95% CI, 0.45–0.91), similar to the unadjusted analysis. Conclusions Despite the changing treatment landscape and the increased number of therapeutic options for metastatic melanoma, these ITCs suggested clinically meaningful improvement in RFS and OS with adjuvant nivolumab compared with a wait-and-watch strategy in high-risk patients with resected melanoma. Acknowledgements Writing and editorial assistance were provided by Kakoli Parai, PhD, and Andrea Lockett of Ashfield Healthcare Communications, funded by Bristol-Myers Squibb Company. Trial Registration NCT02388906 (CheckMate 238), NCT00636168 (EORTC 18071)

Introduction Until recently, adjuvant treatment options for stage III and IV resectable melanoma have been limited. Patients were often managed through routine surveillance. The phase III randomised controlled trial (RCT) CheckMate 238 (238) demonstrated the safety and efficacy of nivolumab as an adjuvant treatment for melanoma in patients with stage IIIB/C or IV disease (American Joint Committee on Cancer [AJCC], 7th edition) versus ipilimumab. The study objective was to estimate the relative efficacy, safety and health-related quality of life (HRQoL) between nivolumab and routine surveillance. Methods Indirect treatment comparisons (ITCs) of nivolumab versus placebo were constructed using data from 238 and EORTC 18071. EORTC 18071 is a phase III RCT comparing ipilimumab with placebo in patients with resected stage IIIA–IIIC melanoma (AJCC, 6th edition). ITCs were performed using the Bucher comparison method and patient-level data for efficacy, safety and HRQoL. Results For the efficacy outcomes, nivolumab performed significantly better than placebo for recurrence-free survival (hazard ratio [HR]: 0.53 [95% confidence interval {CI}: 0.41, 0.68]) and distant metastases-free survival (HR: 0.59 [95% CI: 0.44, 0.78]). Safety ITCs indicated that patients receiving nivolumab had a greater hazard of experiencing an adverse event (AE) and AEs leading to treatment discontinuation, whereas there was a non-significant increased hazard of experiencing a serious AE. HRQoL ITCs showed comparable time to deterioration in 14 of the 15 QLQ-C30 domains; only the dyspnoea domain significantly favoured placebo. Conclusion Nivolumab was associated with significantly improved efficacy outcomes versus placebo, whereas maintaining patient's overall HRQoL. Across the different analysis and populations, there was a high level of consistency in the effect size.

Background\ud \ud Nivolumab with ipilimumab (the Regimen) is the first immuno-oncology combination treatment to demonstrate long-term clinical benefit for advanced melanoma patients. We evaluated the cost effectiveness of the Regimen in this population, with and without the availability of overall survival (OS) data.\ud \ud \ud \ud Methods\ud \ud A partitioned survival model and a Markov state-transition model were developed to estimate the lifetime costs and benefits of the Regimen versus ipilimumab. These models were built with and without the availability of OS data, as only progression-free survival data were available from the head-to-head, phase III trial against ipilimumab at the time of the National Institute for Health and Care Excellence (NICE) submission. Patient utilities and resource use data were sourced from trial data or the literature.\ud \ud \ud \ud Results\ud \ud Incremental cost-effectiveness ratios (ICERs) and absolute costs were similar between the models with and without OS data, but the model with OS data generated more than 1 additional quality-adjusted life-year (QALY) across both treatment arms. In both models, based on list prices, the Regimen was the most cost-effective treatment.\ud \ud \ud \ud Conclusions\ud \ud The analyses show that the Regimen is a cost-effective treatment for advanced melanoma patients in England, and methods to overcome the lack of OS can give reasonable estimates of QALYs gained and ICERs.

Background A goal of adjuvant therapy is to prolong disease-free survival, with the risk of mortality approaching that of the general population. From the literature, recurrences in stage III melanoma are most likely to occur within 3 years after surgery. Mixture-cure models (MCMs) capture survival heterogeneity of cancer patients by assuming they are either “cured” or “uncured”, where cured patients are defined as having a low risk of relapse. Methods We applied MCMs to 3-year recurrence-free survival (RFS) outcomes in patients with resected melanoma treated with adjuvant nivolumab (NIVO) or ipilimumab (IPI) in the double-blind, phase III CheckMate 238 trial (NCT02388906) and assumed that the cured fraction has mortality risks similar to that of the general population. Patient age, sex, and geographic region information from CheckMate 238 and mortality rates from the World Health Organization were used to derive a cohort-level background survival distribution representative of “cured” or “disease-free” patients. Parametric functions were used to model uncured population outcomes. Parameters of the MCM were estimated by means of maximum likelihood methods. Statistical goodness-of-fit metrics and visual inspection showed that generalized gamma and log-logistic distributions were best suited for survival analyses of cured and uncured patients, respectively. Results The 3-year RFS rates were explained by the fraction of patients with a low risk of relapse in the NIVO arm at 55.1% (95% CI, 49.2–60.1) and IPI arm at 39.9% (95% CI, 33.6–46.1). Sensitivity analyses indicated that the estimated fraction of patients with a low risk of relapse was > 50% in the NIVO arm. Estimated RFS distributions of the uncured patients exhibited similar patterns for both arms, implying the differences in RFS rates between treatment arms can be explained with the elicited differences in proportions of patients at high vs low risk of relapse. Conclusions MCM analyses from CheckMate 238 suggested that adjuvant treatment with NIVO leads to a higher proportion of patients with low risk of relapse compared with IPI. Validation of the results from MCMs will include analyses of longitudinal RFS and overall survival data from the EORTC 18071 trial. Clinical trial identification NCT02388906. Editorial acknowledgement Kakoli Parai, PhD, and Andrea Lockett at StemScientific, an Ashfield Company, funded by Bristol-Myers Squibb. Legal entity responsible for the study Bristol-Myers Squibb. Funding Bristol-Myers Squibb. Disclosure J.S. Weber: Honoraria (self), Advisory / Consultancy: AbbVie; Honoraria (self), Advisory / Consultancy, Shareholder / Stockholder / Stock options: AltorBioScience; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Amgen; Research grant / Funding (institution): Astellas Pharma; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Licensing / Royalties, Named on a patent for a PD-1 antibody biomarker: Biodesix; Shareholder / Stockholder / Stock options: Biond; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Bristol-Myers Squibb; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: cCam Biotherapeutics; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Celldex; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Daiichi Sankyo; Honoraria (self), Advisory / Consultancy: Eisai; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Genentech; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: GlaxoSmithKline; Honoraria (self), Advisory / Consultancy: Ichor Medical Systems; Research grant / Funding (institution): Incyte; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Lion Biotechnologies; Honoraria (self), Advisory / Consultancy: Medivation; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Merck; Licensing / Royalties, Named on patent for an ipilimumab biomarker: Moffitt Cancer Center; Honoraria (self), Advisory / Consultancy: Nektar; Honoraria (self), Advisory / Consultancy, Travel / Accommodation / Expenses: Pieris Pharmaceuticals; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Roche; Honoraria (self), Advisory / Consultancy: Sellas Life Sciences; Honoraria (self), Advisory / Consultancy: WindMIL; Honoraria (self), Advisory / Consultancy, Shareholder / Stockholder / Stock options: CytomX Therapeutics; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution), Travel / Accommodation / Expenses: Novartis. M. Kurt: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. M. Edmondson-Jones: Advisory / Consultancy: Bristol-Myers Squibb. A. Amadi: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. M. Lobo: Full / Part-time employment: Bristol-Myers Squibb. A. Moshyk: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. S. Kotapati: Shareholder / Stockholder / Stock options, Full / Part-time employment: Bristol-Myers Squibb. P. Mohr: Honoraria (self), Advisory / Consultancy: Pierre Fabre; Honoraria (self), Advisory / Consultancy: GlaxoSmithKline; Honoraria (self), Advisory / Consultancy: Merck, Sharp & Dohme; Honoraria (self), Advisory / Consultancy: Merck Germany; Honoraria (self), Advisory / Consultancy: Roche; Honoraria (self), Advisory / Consultancy: Bristol-Myers Squibb; Honoraria (self), Advisory / Consultancy: Novartis; Honoraria (self), Advisory / Consultancy: Sanofi.