extRpolateS: A Shiny-Based Interactive Platform for Time-to-Event Data Modeling in Health Technology Assessments
Author(s)
Máté Szilcz, PhD1, Parissa Naghipour, MSc2.
1Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 2Viti Science AB, Stockholm, Sweden.
1Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 2Viti Science AB, Stockholm, Sweden.
OBJECTIVES: To develop extRpolateS, an interactive R-Shiny application that pairs advanced survival-extrapolation methods with an intuitive interface, enabling analysts to generate reliable extrapolations for health-technology-assessments without the need for coding.
METHODS: extRpolateS is a cloud-based platform developed in R Shiny, leveraging the survival, flexsurv and survHE packages for time-to-event modeling, and plotly/ggplot2 for interactive visualizations. The modular interface includes an input module that accepts three types of data: (1) a Kaplan-Meier figure digitized in-app, (2) pre-digitized Kaplan-Meier data (both converted into pseudo-individual patient data using the Guyot algorithm) or (3) empirical trial data. Users can store uploaded data in an integrated database for future analyses. The output module provides descriptive analytics (e.g., Kaplan-Meier curves, proportional hazard checks) and supports a range of modeling approaches, including general parametric (e.g., Weibull), spline-based (normal, hazard, or odds scale), mixture and non-mixture cure, relative survival and piecewise models. All modeling follows the guidelines outlined in the National Institute for Health and Care Excellence (NICE) Technical Support Documents 14 and 21.
RESULTS: Pilot testing with researchers and pharmaceutical company representatives demonstrated that extRpolateS effectively generated time-to-event analyses suitable for integration into health economic models. Preliminary feedback emphasized the platform’s ease of use, particularly its ability to perform statistical analyses accessible to non-experts. Users recognized its potential to streamline survival extrapolation but expressed interest in expanding its capabilities to incorporate health economic models for early economic evaluation.
CONCLUSIONS: extRpolateS is a comprehensive solution for time-to-event analysis, accessible to both researchers and non-experts. It generates robust outputs for health economic models while ensuring ease of use. With its advanced modeling capabilities, extRpolateS improves decision-making in HTAs. Future enhancements, including the integration of health economic modeling, could further expand its impact.
METHODS: extRpolateS is a cloud-based platform developed in R Shiny, leveraging the survival, flexsurv and survHE packages for time-to-event modeling, and plotly/ggplot2 for interactive visualizations. The modular interface includes an input module that accepts three types of data: (1) a Kaplan-Meier figure digitized in-app, (2) pre-digitized Kaplan-Meier data (both converted into pseudo-individual patient data using the Guyot algorithm) or (3) empirical trial data. Users can store uploaded data in an integrated database for future analyses. The output module provides descriptive analytics (e.g., Kaplan-Meier curves, proportional hazard checks) and supports a range of modeling approaches, including general parametric (e.g., Weibull), spline-based (normal, hazard, or odds scale), mixture and non-mixture cure, relative survival and piecewise models. All modeling follows the guidelines outlined in the National Institute for Health and Care Excellence (NICE) Technical Support Documents 14 and 21.
RESULTS: Pilot testing with researchers and pharmaceutical company representatives demonstrated that extRpolateS effectively generated time-to-event analyses suitable for integration into health economic models. Preliminary feedback emphasized the platform’s ease of use, particularly its ability to perform statistical analyses accessible to non-experts. Users recognized its potential to streamline survival extrapolation but expressed interest in expanding its capabilities to incorporate health economic models for early economic evaluation.
CONCLUSIONS: extRpolateS is a comprehensive solution for time-to-event analysis, accessible to both researchers and non-experts. It generates robust outputs for health economic models while ensuring ease of use. With its advanced modeling capabilities, extRpolateS improves decision-making in HTAs. Future enhancements, including the integration of health economic modeling, could further expand its impact.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MSR106
Topic
Clinical Outcomes, Methodological & Statistical Research, Organizational Practices
Disease
No Additional Disease & Conditions/Specialized Treatment Areas