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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Abstract	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.
AbstractList	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. 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 paper, 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 (Suchard et al., 2013).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 paper, 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 (Suchard et al., 2013). 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 paper, 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 (Suchard et al., 2013).
Author	Yang, Jianxiao Schuemie, Martijn J. Suchard, Marc A. Ji, Xiang
AuthorAffiliation	d Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA a Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA f Department of Mathematics, Tulane University, New Orleans, Louisiana, USA e VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA b Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA, USA c Janssen Research and Development, Titusville, NJ, USA
AuthorAffiliation_xml	– name: e VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA – name: f Department of Mathematics, Tulane University, New Orleans, Louisiana, USA – name: b Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA, USA – name: a Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA – name: d Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA – name: c Janssen Research and Development, Titusville, NJ, USA
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SubjectTerms	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
Title	Massive Parallelization of Massive Sample-Size Survival Analysis
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