An Interactive R-Shiny Application for Budget Impact Modeling of Cell and Gene Therapies With Multiple Innovative Payment Scenarios
Author(s)
Manikanta Dasari, Master of pharmacy1, Ayan Chakraborty, Master of Science1, Aniket Das, PhD1, Sahithya Vanjavakam, MPH1, Varun Ektare, MPH2.
1Indence Research Private Limited, North 24 Paraganas, India, 2Indence Research Private Limited, Thane West, India.
1Indence Research Private Limited, North 24 Paraganas, India, 2Indence Research Private Limited, Thane West, India.
OBJECTIVES: Cell and gene therapies (CGTs) are rapidly transforming the treatment landscape for rare and complex diseases. However, their substantial upfront costs present significant challenges for payers aiming to ensure both innovation and affordability. The objective is to develop a user-friendly and adaptable R-Shiny application to model the budget impact (BI) of CGTs under various innovative payment models. The application is designed to support strategic planning and value-based pricing negotiations.
METHODS: The interactive web application, developed using the R-Shiny framework, allows real-time scenario customisation and modelling. Users can compare two market environments: a reference scenario (without CGTs) and a new scenario (with CGTs) over a customisable time horizon (up to five years). The model estimates BI using user-defined inputs for population size, epidemiology, costs, and market share. It supports four distinct payment models: standard payment, discount-based, outcome-based, and annuity models. Annuity logic enables spreading of CGT costs across multiple years, while outcome- and discount-based models apply reductions based on real-world performance or negotiated terms. These models are driven by dynamic matrix inputs, tailored to reflect the specific parameters of each scenario.
RESULTS: The application provides an interactive dashboard presenting BI outputs across scenarios and payment models, including treated populations by year, cost summaries, and net BI. Results are visualised through dynamic plots and formatted summary tables. The tool is optimised for responsiveness, clarity, scenario flexibility to support payer-manufacturer discussions. Internal testing confirmed robustness, with outputs validated against Excel-based BI models. Built-in error handling ensures stability across edge cases and atypical inputs.
CONCLUSIONS: This R-Shiny application addresses a key gap in modelling the affordability and financial planning of CGTs. Its flexible architecture and real-time simulation capabilities enable transparent, comprehensive, and scenario-based evaluation of short-term budget impact. These features support more sustainable, evidence-informed decision-making for high-cost, high-value therapies.
METHODS: The interactive web application, developed using the R-Shiny framework, allows real-time scenario customisation and modelling. Users can compare two market environments: a reference scenario (without CGTs) and a new scenario (with CGTs) over a customisable time horizon (up to five years). The model estimates BI using user-defined inputs for population size, epidemiology, costs, and market share. It supports four distinct payment models: standard payment, discount-based, outcome-based, and annuity models. Annuity logic enables spreading of CGT costs across multiple years, while outcome- and discount-based models apply reductions based on real-world performance or negotiated terms. These models are driven by dynamic matrix inputs, tailored to reflect the specific parameters of each scenario.
RESULTS: The application provides an interactive dashboard presenting BI outputs across scenarios and payment models, including treated populations by year, cost summaries, and net BI. Results are visualised through dynamic plots and formatted summary tables. The tool is optimised for responsiveness, clarity, scenario flexibility to support payer-manufacturer discussions. Internal testing confirmed robustness, with outputs validated against Excel-based BI models. Built-in error handling ensures stability across edge cases and atypical inputs.
CONCLUSIONS: This R-Shiny application addresses a key gap in modelling the affordability and financial planning of CGTs. Its flexible architecture and real-time simulation capabilities enable transparent, comprehensive, and scenario-based evaluation of short-term budget impact. These features support more sustainable, evidence-informed decision-making for high-cost, high-value therapies.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
EE49
Topic
Economic Evaluation
Topic Subcategory
Budget Impact Analysis
Disease
Genetic, Regenerative & Curative Therapies