# A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk

## TL;DR

This paper introduces the fastcmprsk R package, which employs a novel algorithm to enable scalable and efficient analysis of large-scale competing risks data using the Fine-Gray model.

## Contribution

The paper presents a new scalable implementation of the Fine-Gray model in R, significantly improving computational efficiency for large datasets.

## Key findings

- Demonstrates substantial speed improvements over existing methods
- Shows scalability to large biomedical datasets
- Validates accuracy and efficiency through numerical studies

## 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-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.