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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| Published in | Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 12 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
08.02.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1748-670X 1748-6718 1748-6718 |
| DOI | 10.1155/2022/1362913 |
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| Abstract | 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). |
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| AbstractList | 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
O
n
2
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O
n
3
to
O
n
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
>
1
0
4
, 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). 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 O(n 2) or O(n 3) to O(n) 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).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 O(n 2) or O(n 3) to O(n) 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). 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 ( ) or ( ) to ( ) in various steps including numerical integration, risk set calculation, and standard error estimation, where 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 > 10 , 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). 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). 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 O(n2) or O(n3) to O(n) 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). |
| Author | Li, Shanpeng Zhou, Jin Li, Ning Zhou, Hua Li, Gang Wang, Hong |
| AuthorAffiliation | 3 Department of Computational Medicine, University of California at Los Angeles, Los Angeles, CA, USA 2 Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA 1 Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA, USA 4 School of Mathematics and Statistics, Central South University, Changsha, China |
| AuthorAffiliation_xml | – name: 4 School of Mathematics and Statistics, Central South University, Changsha, China – name: 3 Department of Computational Medicine, University of California at Los Angeles, Los Angeles, CA, USA – name: 1 Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA, USA – name: 2 Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA |
| Author_xml | – sequence: 1 givenname: Shanpeng orcidid: 0000-0002-0724-4564 surname: Li fullname: Li, Shanpeng organization: Department of BiostatisticsUniversity of California at Los AngelesLos AngelesCAUSAucla.edu – sequence: 2 givenname: Ning surname: Li fullname: Li, Ning organization: Department of MedicineUniversity of California at Los AngelesLos AngelesCAUSAucla.edu – sequence: 3 givenname: Hong surname: Wang fullname: Wang, Hong organization: School of Mathematics and StatisticsCentral South UniversityChangshaChinacsu.edu.cn – sequence: 4 givenname: Jin orcidid: 0000-0001-7983-0274 surname: Zhou fullname: Zhou, Jin organization: Department of BiostatisticsUniversity of California at Los AngelesLos AngelesCAUSAucla.edu – sequence: 5 givenname: Hua orcidid: 0000-0003-1320-7118 surname: Zhou fullname: Zhou, Hua organization: Department of BiostatisticsUniversity of California at Los AngelesLos AngelesCAUSAucla.edu – sequence: 6 givenname: Gang orcidid: 0000-0002-4753-9420 surname: Li fullname: Li, Gang organization: Department of BiostatisticsUniversity of California at Los AngelesLos AngelesCAUSAucla.edu |
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| Copyright | Copyright © 2022 Shanpeng Li et al. Copyright © 2022 Shanpeng Li et al. 2022 |
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| References | M. Yu (14) 2008; 103 D. Zeng (24) 2005; 11 D. Bates (28) 2007; 2 C. Xu (33) 2020; 93 J. F. Bobb (36) 2021 D. Rizopoulos (2) 2010; 35 F. G. Garre (11) 2008; 171 A. Sattar (5) 2019; 28 M. Sudell (17) 2016; 16 A. B. Owen (34) 2013 D. Rizopoulos (13) 2011; 67 G. T. O'Connor (31) 1995; 152 X. Song (7) 2002; 58 10 R. Elashoff (22) 2016 35 R. M. Elashoff (1) 2017 C. Sudlow (19) 2015; 12 P. Philipson (29) 2018; 1 15 C. Proust-Lima (12) 18 S. Li (38) D. Rizopoulos (27) 2012; 56 D. Rizopoulos (32) H. Lin (23) 2004; 60 W. H. Press (26) 2007 D. Zeng (25) 2005; 33 A. M. Butler (21) 2021 D. Rizopoulos (16) 2012 M. J. Crowther (6) 2013; 13 A. Tsiatis (8) 2004; 14 D. P. Tashkin (30) 2012; 13 C. Y. Wang (9) 2006; 16 R. Henderson (4) 2000; 1 R. M. Elashoff (3) 2008; 64 20 S. Li (37) 2022; 1 |
| References_xml | – volume-title: Monte Carlo Theory, Methods and Examples year: 2013 ident: 34 – ident: 12 article-title: Estimation of extended mixed models using latent classes and latent processes: the r package lcmm doi: 10.18637/jss.v078.i02 – ident: 18 doi: 10.1016/S0140-6736(12)60404-8 – ident: 35 doi: 10.2307/3318671 – volume: 171 start-page: 299 issue: 1 year: 2008 ident: 11 article-title: A joint latent class changepoint model to improve the prediction of time to graft failure publication-title: Journal of the Royal Statistical Society: Series A doi: 10.1111/j.1467-985X.2007.00514.x – volume: 16 start-page: 1 issue: 1 year: 2016 ident: 17 article-title: Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis publication-title: BMC Medical Research Methodology doi: 10.1186/s12874-016-0272-6 – volume: 35 start-page: 1 issue: 9 year: 2010 ident: 2 article-title: JM: an R package for the joint modelling of longitudinal and time-to-event data publication-title: Journal of Statistical Software doi: 10.18637/jss.v035.i09 – ident: 20 doi: 10.1016/j.jclinepi.2015.09.016 – ident: 15 doi: 10.1111/rssa.12348 – volume: 13 start-page: 1 issue: 1 year: 2012 ident: 30 article-title: Comparison of the variability of the annual rates of change in FEV1 determined from serial measurements of the pre-versus post-bronchodilator FEV1 over 5 years in mild to moderate COPD: results of the lung health study publication-title: Respiratory Research – volume: 1 start-page: 5 issue: 2 year: 2018 ident: 29 article-title: joineR: joint modelling of repeated measurements and time-to-event data publication-title: R Package Version – volume-title: Joint Modeling of Longitudinal and Time-to-Event Data year: 2016 ident: 22 doi: 10.1201/9781315374871 – volume: 152 start-page: 87 issue: 1 year: 1995 ident: 31 article-title: A prospective longitudinal study of methacholine airway responsiveness as a predictor of pulmonary-function decline: the normative aging study publication-title: American Journal of Respiratory and Critical Care Medicine doi: 10.1164/ajrccm.152.1.7599868 – ident: 10 doi: 10.1002/sim.7209 – volume: 60 start-page: 295 issue: 2 year: 2004 ident: 23 article-title: Latent pattern mixture models for informative intermittent missing data in longitudinal studies publication-title: Biometrics doi: 10.1111/j.0006-341X.2004.00173.x – volume: 1 start-page: 465 issue: 4 year: 2000 ident: 4 article-title: Joint modelling of longitudinal measurements and event time data publication-title: Biostatistics doi: 10.1093/biostatistics/1.4.465 – volume: 58 start-page: 742 issue: 4 year: 2002 ident: 7 article-title: A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data publication-title: Biometrics doi: 10.1111/j.0006-341X.2002.00742.x – volume: 103 start-page: 178 issue: 481 year: 2008 ident: 14 article-title: Individual prediction in prostate cancer studies using a joint longitudinal survival–cure model publication-title: Journal of the American Statistical Association doi: 10.1198/016214507000000400 – volume: 14 start-page: 809 issue: 3 year: 2004 ident: 8 article-title: Joint modeling of longitudinal and time-to-event data: an overview publication-title: Statistica Sinica – volume: 11 start-page: 151 year: 2005 ident: 24 article-title: Simultaneous modelling of survival and longitudinal data with an application to repeated quality of life measures publication-title: Lifetime Data Analysis doi: 10.1007/s10985-004-0381-0 – volume: 33 start-page: 2132 issue: 5 year: 2005 ident: 25 article-title: Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time publication-title: The Annals of Statistics doi: 10.1214/009053605000000480 – volume: 93 issue: 2 year: 2020 ident: 33 article-title: Semi-parametric joint modeling of survival and longitudinal data: the r package JSM publication-title: Journal of Statistical Software – volume: 1 start-page: 1 year: 2022 ident: 37 article-title: FastJM: semi-parametric joint modeling of longitudinal and survival data publication-title: R Package Version – volume: 28 start-page: 486 issue: 2 year: 2019 ident: 5 article-title: Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection publication-title: Statistical Methods in Medical Research doi: 10.1177/0962280217729573 – volume: 56 start-page: 491 issue: 3 year: 2012 ident: 27 article-title: Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule publication-title: Computational Statistics & Data Analysis doi: 10.1016/j.csda.2011.09.007 – volume: 67 start-page: 819 issue: 3 year: 2011 ident: 13 article-title: Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data publication-title: Biometrics doi: 10.1111/j.1541-0420.2010.01546.x – volume: 13 start-page: 165 issue: 1 year: 2013 ident: 6 article-title: Joint modeling of longitudinal and survival data publication-title: The Stata Journal doi: 10.1177/1536867X1301300112 – volume: 2 start-page: 74 issue: 1 year: 2007 ident: 28 article-title: The lme 4 package publication-title: R Package Version – ident: 32 article-title: The R package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC – volume-title: Numerical Recipes year: 2007 ident: 26 – volume: 64 start-page: 762 issue: 3 year: 2008 ident: 3 article-title: A joint model for longitudinal measurements and survival data in the presence of multiple failure types publication-title: Biometrics doi: 10.1111/j.1541-0420.2007.00952.x – volume: 16 start-page: 235 issue: 1 year: 2006 ident: 9 article-title: Corrected score estimator for joint modeling of longitudinal and failure time data publication-title: Statistica Sinica – volume-title: Joint Modeling of Longitudinal and Time-to-Event Data, volume 151 of Monographs on Statistics and Applied Probability year: 2017 ident: 1 – year: 2021 ident: 36 article-title: turboEM: a suite of convergence acceleration schemes for EM, MM and other fixed-point algorithms – volume-title: Joint Models for Longitudinal and Time-to-Event Data: With Applications in R year: 2012 ident: 16 doi: 10.1201/b12208 – ident: 38 article-title: Efficient algorithms and implementation of a semiparametric joint model for longitudinal and competing risks data: with applications to massive biobank data – volume: 12 issue: 3, article e1001779 year: 2015 ident: 19 article-title: UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age publication-title: PLoS Medicine doi: 10.1371/journal.pmed.1001779 – start-page: 243 volume-title: Databases for Pharmacoepidemiological Research year: 2021 ident: 21 article-title: IBM Marketscan research databases doi: 10.1007/978-3-030-51455-6_20 |
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| SubjectTerms | 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 |
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| Title | Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data |
| URI | https://dx.doi.org/10.1155/2022/1362913 https://www.ncbi.nlm.nih.gov/pubmed/35178111 https://www.proquest.com/docview/2630918609 https://pubmed.ncbi.nlm.nih.gov/PMC8846996 https://escholarship.org/uc/item/30f5m1hm |
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