PCN181 ACCELERATING EARLY COST-EFFECTIVENESS MODELING IN ONCOLOGY USING AN INDICATION-AGNOSTIC WEB-BASED PLATFORM: A RETROSPECTIVE CASE STUDY OF NIVOLUMAB PRE-TREATED METASTATIC SMALL-CELL LUNG CANCER POPULATION

Abstract

To demonstrate the value of a generic cost-effectiveness (CE) modeling platform in oncology for early market access strategy development. With limited clinical data, strategy-making is based on uncertain outcomes and several assumptions. To assess the accuracy in predictive modeling and in identifying key drivers of results, an early analysis based on an initial trial database lock (DBL1) was compared with later, more mature data (DBL2). An internally-developed R/Shiny oncology modeling platform was used for the CE analyses simulating the market access strategy-informing process. The case study, using only published data, compared scenarios using nivolumab data from CheckMate032-DBL1 (“early scenario”) and CheckMate032-DBL2 (“final scenario”) in pre-treated small cell lung cancer (SCLC). The comparator treatment was topotecan, the predominantly-prescribed treatment in pre-treated SCLC. Progression-free and overall survival (PFS, OS), digitized from literature for both treatments, were extrapolated over a 20-year time horizon using parametric survival models. Time on treatment (ToT) was assumed equal to PFS. Model projected nivolumab mean PFS and OS were 4.8 and 26.4 months, respectively, in the early scenario, and 6.9 and 21.7 months in the final scenario. The ICERs differed by 32% between scenarios, mainly due to different OS and ToT. Key inputs affecting differences between-scenarios were ToT and OS. OS differences were expected as DBL1 and DBL2 considered second- and third- or later-line patients, respectively. Between-scenario results variations are expected due to different patient populations, and data maturity levels. Notwithstanding those differences and limitations in including disease-specific elements, the generic modeling platform proves helpful in performing analyses timely, which can guide prioritization of evidence generation by identifying key drivers of results with sparse data (ToT in this case study), assess the impact of parameter variations and alternative modeling approaches for future reimbursement submission-ready models at early stages.