A MODULAR ONCOLOGY REFERENCE MODEL FOR EARLY ECONOMIC EVALUATION: A CASE STUDY IN EGFR-MUTATED NSCLC
Author(s)
Tingting Qu, PhD1, Marko Zivkovic, PhD1, Aaron Crowley, MA2, Agota Szende, PhD3;
1Genesis Research Group, Hoboken, NJ, USA, 2Genesis Research Group, Forest Hills, NY, USA, 3Genesis Research Group, London, United Kingdom
1Genesis Research Group, Hoboken, NJ, USA, 2Genesis Research Group, Forest Hills, NY, USA, 3Genesis Research Group, London, United Kingdom
OBJECTIVES: Early health economic assessment of oncology treatments is increasingly required to inform pricing, evidence generation, and payer engagement prior to availability of mature Phase 3 trial data. However, early-phase oncology trials are frequently single-arm and immature, leading to repeated development of bespoke economic models. This study describes a modular oncology reference model for early economic evaluation and demonstrates its application to a case study employing an early phase trial and an external control arm (ECA).
METHODS: The modular oncology reference model was developed using a partitioned survival framework with three health states (progression-free, progressed disease, and death), with modular inputs enabling rapid adaptation and an architecture supporting future AI-assisted data sourcing and parameterization. The case study assessed the cost-effectiveness of patritumab deruxtecan (HER3-DXd) in EGFR-mutated non-small cell lung cancer (NSCLC) in the US. The overall survival (OS) and progression-free survival (PFS) of HER3-DXd were sourced from the HERTHENA-Lung01 Phase 2 trial (NCT04619004), while the treatment regimens, real-world OS and PFS of the ECA matching to the trial population was derived from de-identified electronic health records (PMID: 38958845).
RESULTS: The model-derived PFS hazard ratio (HR) between HER3-DXd and ECA of 0.76 validated the PFS HR observed in the HERTHENA-Lung02 Phase 3 trial (0.77; 95% confidence interval [CI], 0.63-0.94; P=.011). Compared with the ECA, HER3-DXd was associated with an additional 0.31 life years (LYs) and 0.17 quality-adjusted life-years (QALYs) gained per patient lifetime. Key drivers of cost-effectiveness were the cost of HER3-DXd and health state utilities.
CONCLUSIONS: Our analysis illustrated that combining reusable model structures with indication-specific early phase trial inputs, real-world ECA and architecture designed for future AI-assisted automation enables a reference model platform for timely, high-quality decision support for oncology assets in early stage.
METHODS: The modular oncology reference model was developed using a partitioned survival framework with three health states (progression-free, progressed disease, and death), with modular inputs enabling rapid adaptation and an architecture supporting future AI-assisted data sourcing and parameterization. The case study assessed the cost-effectiveness of patritumab deruxtecan (HER3-DXd) in EGFR-mutated non-small cell lung cancer (NSCLC) in the US. The overall survival (OS) and progression-free survival (PFS) of HER3-DXd were sourced from the HERTHENA-Lung01 Phase 2 trial (NCT04619004), while the treatment regimens, real-world OS and PFS of the ECA matching to the trial population was derived from de-identified electronic health records (PMID: 38958845).
RESULTS: The model-derived PFS hazard ratio (HR) between HER3-DXd and ECA of 0.76 validated the PFS HR observed in the HERTHENA-Lung02 Phase 3 trial (0.77; 95% confidence interval [CI], 0.63-0.94; P=.011). Compared with the ECA, HER3-DXd was associated with an additional 0.31 life years (LYs) and 0.17 quality-adjusted life-years (QALYs) gained per patient lifetime. Key drivers of cost-effectiveness were the cost of HER3-DXd and health state utilities.
CONCLUSIONS: Our analysis illustrated that combining reusable model structures with indication-specific early phase trial inputs, real-world ECA and architecture designed for future AI-assisted automation enables a reference model platform for timely, high-quality decision support for oncology assets in early stage.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MSR214
Topic
Methodological & Statistical Research
Disease
SDC: Oncology