A PORTFOLIO MODELING FRAMEWORK FOR HEOR DECISION MAKING
Author(s)
Sonja Sorensen, MPH1, Ariel Sun, MA2, Neda Aminnejad, PhD3;
1Thermo Fisher Scientific, Bethesda, MD, USA, 2Thermo Fisher Scientific, San Francisco, CA, USA, 3Thermo Fisher Scientific, Toronto, ON, Canada
1Thermo Fisher Scientific, Bethesda, MD, USA, 2Thermo Fisher Scientific, San Francisco, CA, USA, 3Thermo Fisher Scientific, Toronto, ON, Canada
Presentation Documents
OBJECTIVES: To develop and apply a standardized, scalable portfolio modeling framework to estimate life-year (LY) and quality-adjusted life-year (QALY) shortfall, as well as potential health gains of new therapies versus standard of care (SoC) and the general population across multiple indications. The framework is designed to support HEOR-informed portfolio prioritization.
METHODS: An Excel-based portfolio model was developed to capture a broad range of diseases using commonly applied health economic structure, including partitioned survival, state transition, and non-mortality-driven response-based models. For each indication, users select from predefined structural options based on natural history and available clinical endpoints, and specify key data features such as overall survival, composite endpoints (e.g., event-free survival), or response-based outcomes. To ensure comparability of outcomes across indications, standardized methods were applied for long-term extrapolation, health state utility assignment, and benchmarking against age- and sex-matched general population outcomes. SoC efficacy and survival data were obtained from pivotal trials, clinical guidelines, and HTA submissions. Hypothetical pipeline assets were modeled by applying assumed hazard ratios to extrapolated SoC curves. The model incorporates automated workflows to support efficient results generation, cross-indication comparisons, and reporting.
RESULTS: The framework produces indication- and portfolio-level estimates of LY and QALY shortfall versus SoC and the general population, as well as incremental LY and QALY gains for pipeline assets. Case-study applications demonstrate how the standardized framework supports direct and methodologically consistent comparison of unmet need across multiple hematology indications.
CONCLUSIONS: This portfolio modeling framework provides a generalizable approach for assessing and quantifying unmet need and disease burden across diverse indications. By incorporating QALY shortfall and potential health gains within a semi-automated modeling and reporting platform, the framework enables direct and methodologically consistent cross-indication comparison of unmet need, addressing a key challenge in portfolio planning and prioritization.
METHODS: An Excel-based portfolio model was developed to capture a broad range of diseases using commonly applied health economic structure, including partitioned survival, state transition, and non-mortality-driven response-based models. For each indication, users select from predefined structural options based on natural history and available clinical endpoints, and specify key data features such as overall survival, composite endpoints (e.g., event-free survival), or response-based outcomes. To ensure comparability of outcomes across indications, standardized methods were applied for long-term extrapolation, health state utility assignment, and benchmarking against age- and sex-matched general population outcomes. SoC efficacy and survival data were obtained from pivotal trials, clinical guidelines, and HTA submissions. Hypothetical pipeline assets were modeled by applying assumed hazard ratios to extrapolated SoC curves. The model incorporates automated workflows to support efficient results generation, cross-indication comparisons, and reporting.
RESULTS: The framework produces indication- and portfolio-level estimates of LY and QALY shortfall versus SoC and the general population, as well as incremental LY and QALY gains for pipeline assets. Case-study applications demonstrate how the standardized framework supports direct and methodologically consistent comparison of unmet need across multiple hematology indications.
CONCLUSIONS: This portfolio modeling framework provides a generalizable approach for assessing and quantifying unmet need and disease burden across diverse indications. By incorporating QALY shortfall and potential health gains within a semi-automated modeling and reporting platform, the framework enables direct and methodologically consistent cross-indication comparison of unmet need, addressing a key challenge in portfolio planning and prioritization.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MSR92
Topic
Methodological & Statistical Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas