Variable selection in joint modeling of skewed longitudinal data and discrete failure time data

Music City Center 

Joint modeling of longitudinal data and survival data has gained great attention over the last few decades. We study joint analysis of skewed longitudinal data and discrete failure time data, and conduct grouped variable selection in this framework. A joint model is proposed with a shared frailty to characterize the dependence between the two types of responses, where the longitudinal response is modeled with a log-normal mixed-effects submodel and the survival time is modeled with a complementary log-log submodel. Penalized likelihood-based approaches are developed to simultaneously select significant covariates and estimate their effects on the two types of responses. A Monte Carlo EM (MCEM) method is used for the implantation. Our simulation study shows that these methods perform well in both variable selection and parameter estimation. A real-life data application to the LIFE study is provided as an illustration.

joint modeling

Skewed longitudinal data

discrete failure time data

grouped variable selection



Monte Carlo EM