Joint Modeling of Survival and Longitudinal Data

It is prevalent in clinical follow-up studies, either randomized experiments or observational studies, that time-to-event and longitudinal data are both collected. Investigators are interested in studying the effect of longitudinally collected time-variant covariate on the time-to-event outcome or the longitudinal outcomes subject to nonrandom truncation by the time-to-event. Traditional statistical methods often lead to biased inference in dealing with endogenous time-variant longitudinal covariates for survival data analysis or in longitudinal data analysis if ignoring the informative dropout triggered by time-to-event data.

Joint modeling of survival and longitudinal data has become a critical tool for analyzing such data in a wide range of applications. Many innovative methodologies have been developed for joint modeling in the last 30 years. 

Our Research:

• Dong, J., Cao J., Gill, J., Miles, C., Plumb, T. (2021). Functional joint models for chronic kidney disease in kidney transplant recipients. Statistical Methods in Medical Research. 0(0):1-12. 
• Shi, H., Dong, J., Wang, L., and Cao, J. (2021). Functional principal component analysis for longitudinal data with informative dropout. Statistics in Medicine. 40(3): 712-724.
• Zheng, C., and Liu, L. (2021). Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach. Biometrics. 
• Chu, C., Zhang, Y., and Tu, W. (2020). Stochastic functional estimates in longitudinal models with interval-censored anchoring events. Scandinavian Journal of Statistics. 47 (3): 638-661.
• Chu, C., Zhang, Y., and Tu, W. (2019). Distribution-free estimation of local growth rates around interval censored anchoring events. Biometrics. 75: 463-474.
• Dong, J., Wang, S., Wang, L., Gill, J., and Cao, J. (2019). Joint modeling for organ transplantation outcomes for patients with diabetes and the end-stage renal disease. Statistical Methods in Medical Research. 28(9): 2724-2737.
• Liu, L., Zheng, C., and Kang, J. (2018). Exploring causality mechanism in the joint analysis of longitudinal and survival data. Statistics in Medicine. 37: 3733-3744.