Massive Parallelization of Massive Sample-Size Survival Analysis

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...

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Vydáno v:Journal of computational and graphical statistics Ročník 33; číslo 1; s. 289 - 302
Hlavní autoři: Yang, Jianxiao, Schuemie, Martijn J., Ji, Xiang, Suchard, Marc A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Taylor & Francis 2024
Taylor & Francis Ltd
Témata:
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
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Shrnutí: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.
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2023.2213279