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Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications
Takeshi Emura1,2, Hirofumi Michimae3, Shigeyuki Matsui2,4
1Biostatistics Center, Kurume University, Kurume 830-0011, Japan.
Entropy (Basel, Switzerland)
|May 28, 2022
Summary
This tutorial guides developers in creating dynamic cancer risk prediction web applications. It uses a joint frailty-copula model and R packages to update predictions with new data, improving clinical decision-making.
Area of Science:
- Biostatistics
- Medical Informatics
- Oncology
Background:
- Clinical risk prediction formulas for cancer patients require dynamic updates using intermediate events like tumor progression.
- Increasing availability of individual patient data (IPD) necessitates dynamic prediction models that handle between-study heterogeneity.
- Existing literature lacks comprehensive guidance on developing, validating, and publishing dynamic prediction formulas.
Purpose of the Study:
- To provide a tutorial for building a web-based application for dynamic risk prediction in cancer patients.
- To demonstrate the development and online publication of a dynamic prediction tool using R packages.
- To enable users to dynamically update cancer risk predictions based on individual patient data and intermediate events.
Main Methods:
- Utilized a joint frailty-copula model to analyze overall survival and time to tumor progression.
- Employed the R packages 'joint.Cox' and 'Shiny' for model development and web application creation.
- Validated the methodology using a multi-study breast cancer patient dataset.
Main Results:
- Successfully developed a tutorial for creating a dynamic risk prediction web application.
- Demonstrated the process of publishing the web application online for user accessibility via computers and smartphones.
- Empowered developers to build similar online tools using their own datasets.
Conclusions:
- A joint frailty-copula model, implemented with 'joint.Cox' and 'Shiny', facilitates the creation of dynamic cancer risk prediction tools.
- Web-based applications enhance the accessibility and usability of dynamic prediction formulas for clinicians and researchers.
- This tutorial provides a reproducible framework for developing and deploying personalized, data-driven cancer risk prediction tools.
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