Massive Parallelization of Massive Sample-Size Survival Analysis

• Published in: Journal of computational and graphical statistics Vol. 33; no. 1; pp. 289 - 302
• Main Authors: Yang, Jianxiao, Schuemie, Martijn J., Ji, Xiang, Suchard, Marc A.
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 2024; Taylor & Francis Ltd
• Subjects: Algorithms; Cox proportional hazards model; Fine-Gray model; Graphics processing unit; Graphics processing units; Parallel processing; Regression models; Regularized regression; Statistical models; Survival; Survival analysis; Survival analysis; Regularized regression; Cox proportional hazards model; Fine-Gray model; Graphics processing unit
• ISSN: 1061-8600; 1537-2715
• DOI: 10.1080/10618600.2023.2213279

Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this article, we use Graphics Processing Units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders of magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. The above implementation is available in the open-source R package Cyclops.