Tutorial: translating a validated breast cancer prediction model into a web-based decision aid using R Shiny.

Risk prediction model development has expanded rapidly, but few models have been translated into patient-facing tools that support informed decision-making. Existing breast cancer models provide no guidance on how to incorporate risk into decisions around screening or prevention medications, limiting their practical utility. To address this gap, we previously developed and validated a competing risk regression model that simultaneously predicts breast cancer and non-breast cancer death and then developed a web-based decision aid application that integrates this model with interactive, personalized information on screening and prevention medications. Using this application as a case study, we present a framework for developing an online decision aid using R Shiny. While prior Shiny tutorials have focused on simple applications or calculators, practical guidance for integrating risk prediction into multi-page, interactive decision support tools remains limited. In our tutorial, we describe key components of development, including application structure, user input collection, real-time calculation of individualized risk estimates, and presentation of results in a clear, interpretable format. We also demonstrate implementation of core Shiny functionalities, including reactive values for dynamic updates, data visualization techniques to contextualize risk estimates, and use of the observeEvent function to enable conditional display and navigation. Through this tutorial, we illustrate how risk calculators can be extended into comprehensive, dynamic and clinically useful tools that support informed decision-making.