fastcmprsk swMATH ID: 28753 Software Authors: Eric S Kawaguchi, Jenny I Shen, Gang Li, Marc A Suchard Description: A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk. 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-Gray 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. Homepage: https://arxiv.org/abs/1905.07438 Dependencies: R Keywords: arXiv_stat.CO; R; R package; Fine-Gray model; inverse-censoring probability; large-scale data; scalable computing; semi-parametric modeling; survival analysis; time-to-event data Related Software: crrSC; crrp; doParallel; crrstep; cmprsk; timereg; riskRegression; R Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk Eric S Kawaguchi, Jenny I Shen, Gang Li, Marc A Suchard 2019