期刊
STATISTICAL ANALYSIS AND DATA MINING
卷 15, 期 3, 页码 314-321出版社
WILEY
DOI: 10.1002/sam.11563
关键词
bags of little bootstraps; big data; EMR; linear mixed models; longitudinal data; parallel and distributed computing
类别
资金
- Division of Mathematical Sciences [DMS-2054253]
- National Heart, Lung, and Blood Institute [HL150374]
- National Human Genome Research Institute [HG006139]
- National Institute of Diabetes and Digestive and Kidney Diseases [DK106116]
- National Institute of General Medical Sciences [GM141798]
The study introduces a highly efficient statistical method for analyzing very large longitudinal datasets, showing significant advantages over traditional methods.
Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers.
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