Statistics, Department of
The R Journal
Date of this Version
12-2020
Document Type
Article
Citation
The R Journal (December 2020) 12(2); Editor: Michael J. Kane
Abstract
Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine and Gray (1999) proportional subdistribution hazards model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan algorithm to significantly reduce the computational complexity for parameter estimation by exploiting the structure of the subject-specific risk sets. Numerical studies compare the speed and scalability of our implementation to current methods for unpenalized and penalized Fine-Gray regression and show impressive gains in computational efficiency
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International