Assessing the Impact of Modeling Non-Disease-Related Mortality on Long-Term Survivorship Rates in Previously Untreated Advanced Melanoma: A Case Study from CheckMate 067
Author(s)
Paly V1, Mohr P2, Larkin J3, Middleton M4, Youn JH5, Remiro-Azocar A6, Baio G7, Moshyk A8, Kotapati S8, Hamilton M8, Kurt M8
1ICON plc, New York, NY, USA, 2Elbe Klinikum Buxtehude, Buxtehude, NY, Germany, 3The Royal Marsden NHS Foundation Trust, London, UK, 4University of Oxford, Oxford, UK, 5ICON plc, Marlow, Bucks, UK, 6University College London, London, SRY, UK, 7University College London, London, UK, 8Bristol Myers Squibb, Princeton, NJ, USA
OBJECTIVES: Mixture cure models (MCM) provide a useful framework to explore survival heterogeneity and to estimate the proportion of long-term survivors (LTS) in a trial. We examined the sensitivity of LTS rates and resulting survival projections to modeling background mortality in an MCM for previously untreated advanced melanoma patients in the CheckMate 067 trial. METHODS: For each arm, MCMs were fitted to 5-year progression-free survival (PFS) and overall survival (OS) data from the trial under two scenarios. In the first scenario, LTS were subject to no disease-related mortality and their background hazard was modeled using publicly available age, gender and country specific lifetables for the general population. In the second scenario, LTS were assumed to follow a survival trend similar to that of complete responders (CR) in the trial. In both scenarios, disease-related time-to-event trends for non-LTS were modeled using parametric distributions. After estimation of overall LTS rates, lifetime mean PFS and mean OS were calculated and Bayes’ rule was employed to indirectly derive LTS rates among 5-year survivors. RESULTS: The difference in the estimated LTS rates between the two scenarios was marginal in each arm: 1.6-2.0% for overall population and 0.4-1.6% for 5-year survivors. Under both scenarios considered for LTS background mortality, the difference between LTS rates estimated from PFS versus OS data was lower for 5-year survivors (1.0-11.7% difference across arms) than for the overall population (13.9-16.1% difference across arms). Modeling background mortality with CR survival trend rather than general population mortality generated more conservative mean PFS (14-36 months shorter) and mean OS (5-26 months shorter) estimates across arms. CONCLUSIONS: Relaxing the assumption that LTS experience no disease-related mortality can render MCMs more acceptable to health technology assessment reviewers. In this study, it had substantial impact on mean survival projections despite marginal changes in estimated LTS rates.
Conference/Value in Health Info
2021-05, ISPOR 2021, Montreal, Canada
Value in Health, Volume 24, Issue 5, S1 (May 2021)
Code
PCN193
Topic
Methodological & Statistical Research
Disease
Oncology