A0872
Title: Bayesian joint analysis of longitudinal data and interval-censored failure time data
Authors: Yuchen Mao - University of South Carolina (United States)
Lianming Wang - University of South Carolina (United States) [presenting]
xuemei sui - University of South Carolina (United States)
Abstract: Joint modeling of longitudinal measurements and survival time has gained great attention in statistics literature in the last two decades. Most of the existing works focus on the joint analysis of the longitudinal response and right-censored survival time. We propose a new frailty model for joint analysis of a longitudinal response and interval-censored survival time. Such data commonly arise in real-life studies where participants are examined at periodical or irregular follow-up times. The proposed joint model has the following appealing properties: (1) the regression coefficients can be interpreted as the marginal effects in both the longitudinal model and the survival model components and (2) the statistical association between the longitudinal response and the survival response can be described and quantified using several association measures in simple explicit forms. The adoption of splines allows us to model the unknown baseline functions with only a finite number of unknown coefficients while providing great modeling flexibility. An efficient Gibbs sampler is developed for posterior computation. Simulation results show that the proposed method performs very well in estimating all the regression parameters and the unknown baseline functions. The proposed method is further illustrated by a real-life application to the patient data from the Aerobics Center Longitudinal Study.
