Presentation (CTI)

OBJECTIVES: Demand for early economic evaluation in oncology is increasing, driven by expanding pipelines, multi-indication treatments, and requirements for timely evidence-generation insights. However, survival analysis and cost-effectiveness workflows are often redeveloped despite recurring methods, resulting in duplication and inconsistent implementation. Decentralised storage and version-control issues also limit communication, reuse and auditability. Integrated platforms for conducting, recording, and reporting analyses may improve consistency, transparency and actionability. This study aimed to develop an integrated modelling platform for early economic evaluation of oncology treatments.
METHODS: A workflow including indication specification, survival analysis per NICE guidelines, partitioned survival modelling, sensitivity analysis, and reporting was mapped into discrete elements. Base calculations for each element were implemented in R and interfaced with R Shiny; Shiny modules were used alongside a bespoke data hub function to manage code complexity and to link data across workflow elements. Analysis was stored in SQL-databases. Cross validity exercises were conducted with recent cost-effectiveness evaluations in non-small cell lung cancer.
RESULTS: The platform established an end-to-end workflow for early economic evaluation of oncology treatments in a single environment. The R Shiny interface separated user-facing decisions from tested calculation logic, reducing repeat implementation across similar analyses. Analysis storage enabled saving, rerunning and comparison of outputs, improving traceability, collaboration and comparability. Cross validity results showed high agreement between platform estimated and published outputs: restricted mean survival (R²>0.95; root mean squared error [RMSE] <10%); costs and quality-adjusted life years (R²>0.90; RMSE<15%).
CONCLUSIONS: This work demonstrates how R can serve as a basis for scalable, transparent, and collaborative economic evaluation platforms, transforming how evidence is generated, shared, and used to inform research prioritisation and decision-making. Moreover, it illustrates the potential of R Shiny for developing interactive analytical environments that guide users through complex modelling tasks and sets out a framework for development of similar approaches in other disease areas.