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Chen, Ding-Geng; Lio, Yuhlong; Wilson, Jeffrey R.

Bayesian approach for joint modeling longitudinal data and survival data simultaneously in public health studies. (English) Zbl 07619967

Lio, Yuhlong (ed.) et al., Bayesian inference and computation in reliability and survival analysis. Cham: Springer. Emerg. Top. Stat. Biostat., 343-355 (2022).
Summary: This chapter is aimed to overview the joint modeling through the harmonization of longitudinal data and time-to-event data with a Bayesian approach. We considered a randomized clinical trial in which both longitudinal data and survival data were collected to compare the efficacy and the safety of two antiretroviral drugs in treating patients who had failed or were intolerant of zidovudine (AZT) therapy. Using these data, we demonstrated the advantages of the Bayesian joint modeling over the classical approach of separately analyzing these types of data with Cox proportional hazard model and longitudinal linear mixed-effects model. We found that the Bayesian joint modeling can better address information loss on outcome-dependent missingness, which can preserve information from both longitudinal data and time-to-event data. The Bayesian joint modeling can produce unbiased estimates and retain higher statistical power for public health data analysis.
For the entire collection see [Zbl 1492.62020].

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
62N05 Reliability and life testing

Keywords:

HIV/AIDS; zidovudine (AZT) therapy; survival data; time-to-event; time-to-death due to HIV/AIDS; longitudinal data; CD4 counts; joint modeling; Cox proportional hazard model; linear mixed-effects model; clinical trials; Bayesian approach; mixed-effects model; time-varying; hazard ratio; intervention effectiveness; missing values; open source; multiple imputation; fixed effects; random effects; Markov chain Monte Carlo

Software:

nlme; lme4; JM; JMbayes; survival
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Full Text: DOI

References:

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