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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Software Application Profile: dynamicLM-a tool for performing dynamic risk prediction using a landmark supermodel for
Anya H Fries1, Eunji Choi1, Julie T Wu2
1Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
International Journal of Epidemiology
|September 6, 2023
Summary
The dynamicLM R package offers a flexible tool for predicting disease progression using landmark models with competing risks. It enhances personalized medicine by providing dynamic survival data assessments.
Area of Science:
- Biostatistics
- Medical Informatics
- Computational Biology
Background:
- Dynamic prognosis assessment is crucial for personalized medicine.
- Landmark models offer powerful tools for predicting disease progression.
- Existing frameworks lack flexibility for diverse outcomes like competing risks.
Purpose of the Study:
- To introduce dynamicLM, a user-friendly R package for landmark models.
- To facilitate dynamic prediction of survival data under competing risks.
- To provide a comprehensive tool for data preparation, modeling, prediction, and performance evaluation.
Main Methods:
- The dynamicLM R package implements cause-specific landmark models.
- It incorporates time-varying covariates and time-dependent predictor effects.
- The package supports survival data with or without competing risks.
Main Results:
- dynamicLM provides functions for data preparation, model fitting, and prediction.
- It includes tools for evaluating predictive performance, such as time-dependent AUC and Brier Score.
- The package is available on GitHub for public access and use.
Conclusions:
- dynamicLM addresses the need for a flexible and comprehensive tool for landmark modeling.
- It enables dynamic prediction of survival outcomes in the presence of competing risks.
- This R package advances the application of landmark models in personalized medicine and survival analysis.
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