Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data

Semiparametric joint models of longitudinal and competing risk data are computationally costly, and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and compet...

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Vydané v:Computational and mathematical methods in medicine Ročník 2022; s. 1 - 12
Hlavní autori: Li, Shanpeng, Li, Ning, Wang, Hong, Zhou, Jin, Zhou, Hua, Li, Gang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Hindawi 08.02.2022
Predmet:
Algorithms
Biological Specimen Banks - statistics & numerical data
Bronchodilator Agents - therapeutic use
Computational Biology
Computer Simulation
Data Interpretation, Statistical
Disease Progression
Humans
Longitudinal Studies
Models, Statistical
Primary Health Care - statistics & numerical data
Pulmonary Disease, Chronic Obstructive - physiopathology
Pulmonary Disease, Chronic Obstructive - therapy
Risk Assessment
Smoking Cessation - statistics & numerical data
Software
ISSN:1748-670X, 1748-6718, 1748-6718
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Shrnutí:Semiparametric joint models of longitudinal and competing risk data are computationally costly, and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risk survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from On2 or On3 to On in various steps including numerical integration, risk set calculation, and standard error estimation, where n is the number of subjects. Using both simulated and real-world biobank data, we demonstrate that these linear scan algorithms can speed up the existing methods by a factor of up to hundreds of thousands when n>104, often reducing the runtime from days to minutes. We have developed an R package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and competing risk time-to-event data and made it publicly available on the Comprehensive R Archive Network (CRAN).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Reinoud Maex
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2022/1362913