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 department faculty (Drs. Dong, Zhang, and Zheng) have made some contributions to this field.
- 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. doi: 10.1177/09622802211009265.
- 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. doi: 10.1111/biom.13475
- 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.