The R Journal: article published in 2020, volume 12:2

A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk PDF download
Eric S. Kawaguchi, Jenny I. Shen, Gang Li and Marc A. Suchard , The R Journal (2020) 12:2, pages 163-172.

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 computation ally 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.

Received: 2020-01-16; online 2021-01-14, supplementary material, (1.4 KiB)
CRAN packages: fastcmprsk, cmprsk, riskRegression, timereg, survival, crrSC, crrstep, crrp, glmnet, ncvreg, Cyclops, doParallel
CRAN Task Views implied by cited CRAN packages: Survival, MachineLearning, ClinicalTrials, Econometrics, SocialSciences

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Eric S. Kawaguchi and Jenny I. Shen and Gang Li and Marc A.
  title = {{A Fast and Scalable Implementation Method for Competing
          Risks Data with the R Package fastcmprsk}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-010},
  url = {},
  pages = {163--172},
  volume = {12},
  number = {2}