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Objectives: Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non–small-cell lung cancer (NSCLC). Methods: Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs—landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)—were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. Results: For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). Conclusions: We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Highlights: Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up. Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes. Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data. Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting. Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup. [ABSTRACT FROM AUTHOR]

Purpose: Standard-of-care treatments for patients with resected stage III/IV melanoma include the immuno-oncology (IO) agents nivolumab (NIVO) and ipilimumab (IPI). This study used mixture cure models (MCMs) to estimate cure rates among patients treated with NIVO or IPI in the phase III CheckMate 238 (ClinicalTrials.gov identifier: NCT02388906) and European Organization for Research and Treatment of Cancer (EORTC) 18071 (ClinicalTrials.gov identifier: NCT00636168) trials, and to assess the impact of use of adjuvant immunotherapy on cure rates versus watchful waiting., Methods: MCMs were applied to patient-level recurrence-free survival data from CheckMate 238 and EORTC 18071. Cured patients were assumed to experience no disease recurrence and mortality risks similar to the general population. Uncured patients were at risk of disease recurrence and all-cause death. The survival trend of the cured patients was estimated using life expectancy data for a general population with the same baseline demographic characteristics. A regression model assessed the odds ratios (ORs) of cure across key subgroups on the basis of baseline characteristics of the study populations., Results: In CheckMate 238, estimated cure rates were 48.3% (95% CI, 41.8 to 54.9) with NIVO and 38.2% (95% CI, 32.7 to 44.1) with IPI. In EORTC 18071, estimated cure rates were 38.0% (95% CI, 32.1 to 44.2) with IPI and 29.2% (95% CI, 24.4 to 34.6) with placebo. In the indirect comparison of the two trials, the odds of cure were significantly higher with NIVO than with placebo (OR, 2.33 [95% CI, 1.49 to 3.65])., Conclusion: Analyses involving two large phase III trials investigating adjuvant IO treatment for resected melanoma demonstrate higher cure rates for both NIVO and IPI than placebo, with NIVO providing the highest cure rate. Similar cure rates were estimated for patients treated with IPI in both trials, despite staging and dosing differences.

Introduction: New requirements in Canada's pricing processes for patented drugs may exacerbate delays in regulatory and reimbursement reviews. This study seeks to better understand the impact of any additional delays on non-small cell lung cancer (NSCLC) patients by measuring the following: (a) durations and outcomes of regulatory and reimbursement reviews of NSCLC drugs in Canada and reference countries; (b) delays in Canada's reviews of three NSCLC drugs (nivolumab, afatinib, and pemetrexed [NAP]); and (c) estimating clinical, patient, and economic impacts of delays in Canada's reviews on access to NAP., Methods: Information from the Context Matters database and the literature (2005-2020) was used to evaluate the durations and outcomes of reimbursement reviews of NSCLC drugs in Canada and comparator countries. Public information was used to assess delays in Canada's reviews of NAP. Empirical modeling with data from the literature and the Southern Alberta Lung Cancer database was used to estimate the impact of delays in Canada's NAP reviews on patients (i.e., as losses in person-years of life and quality-adjusted life-years [QALYs])., Results: Regulatory and reimbursement reviews in countries of interest take 12-18 months. In Canada, reviews of NSCLC drugs took 216 days (median), with a 24% rejection rate (mean = 19%). Delays in NAP reviews ranged from 5 to 94 days at Health Canada, 0-80 days at CADTH/pCODR, and 12-797 days in Canadian provinces. These delays may have affected 6400 patients, who lost up to 1740 person-years of life and 1122 QALYs (valued at CA$112 million)., Conclusion: Changes to Canada's prescription drug pricing processes may prolong reviews., (© 2022. The Author(s).)