Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly

XIE Wei-hua; YU Xiao-jin; DAI Pin-yuan; SUN Jin-fang; WANG Li-na; QIN Yu; WU Ming; ZHAO Jian

doi:10.16462/j.cnki.zhjbkz.2021.01.014

Volume 25 Issue 1

Jan. 2021

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XIE Wei-hua, YU Xiao-jin, DAI Pin-yuan, SUN Jin-fang, WANG Li-na, QIN Yu, WU Ming, ZHAO Jian. Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014

Citation:

XIE Wei-hua, YU Xiao-jin, DAI Pin-yuan, SUN Jin-fang, WANG Li-na, QIN Yu, WU Ming, ZHAO Jian. Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014

Citation:

XIE Wei-hua, YU Xiao-jin, DAI Pin-yuan, SUN Jin-fang, WANG Li-na, QIN Yu, WU Ming, ZHAO Jian. Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 72-77. doi: 10.16462/j.cnki.zhjbkz.2021.01.014

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Application of a bayesian joint model for the association of changes in pulse pressure and all-cause mortality in the elderly

doi: 10.16462/j.cnki.zhjbkz.2021.01.014

1.
Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China
2.
Department of Chronic Non-Communicable Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China

Funds:

National Natural Science Foundation of China 81673274

More Information

Corresponding author: YU Xiao-jin, E-mail: xiaojinyu@seu.edu.cn
Received Date: 2020-07-11
Rev Recd Date: 2020-11-10
Publish Date: 2021-01-10

Abstract

Abstract

Objective To explore application strategies and statistical performance when fitting Bayesian joint models to interrelate longitudinal and survival outcomes, and provide methodological guidance for the analysis of similar data. Methods A nonlinear mixed-effects model of longitudinally measured pulse pressure was fitted with a natural cubic spline function, and a B-spline method was used to construct the baseline hazard function for all-cause survival data. A Bayesian joint model was established by associating two processes through shared random effects and Gibbs sampling was used to calculate model parameters. The results of Bayesian joint model were compared with the classical two-stage joint model. Results The Bayesian joint model showed a higher baseline pulse pressure (α₁=0.72, 95% CI: 0.43-1.13) and faster rise in pulse pressure in years 0 to 3, 3 to 6, and 6 to 9 (α₂₁=0.34, 95% CI: 0.20-0.45; α₂₂= 0.45, 95% CI: 0.10-0.75; α₂₃=0.42, 95% CI: 0.24-0.62), which may contribute to a higher risk of all-cause death in the elderly. Bayesian and two-stage joint models were consistent in the direction of parameter point estimates, with Bayesian interval widths greater than two-stage methods. Conclusions Bayesian joint model is a reasonably valid statistical method for joint analysis of longitudinal and survival data when there is an association between them. This study has shown that both high baseline pulse pressure and rapid increases in pulse pressure in older adults are associated with higher all-cause mortality.
- Longitudinal data,
- Survival data,
- Pulse pressure in the elderly,
- All-cause mortality,
- Bayesian joint model,
- Two-stage joint model

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References(21)

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