Abstract
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.
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Acknowledgments
This work is based on the research supported partially by the National Research Foundation of South Africa (Grant Number 127727) and the South African National Research Foundation (NRF) and South African Medical Research Council (SAMRC) (South African DST-NRF-SAMRC SARChI Research Chair in Biostatistics, Grant Number 114613).
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Chen, DG., Lio, Y., Wilson, J.R. (2022). Bayesian Approach for Joint Modeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies. In: Lio, Y., Chen, DG., Ng, H.K.T., Tsai, TR. (eds) Bayesian Inference and Computation in Reliability and Survival Analysis. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-88658-5_16
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