Statistics and Data Science Seminar
Yuan Xu
Washington University at St. Louis
Joint Modeling of Longitudinal and Time to Event Data with Random Changepoints
Abstract: We develop a joint model of longitudinally observed cognitive data and survival data to the onset of dementia. We incorporate latent random change points in the model representing an accelerated cognitive decline prior to the onset of dementia. We aim to investigate how different covariates of subjects, such as baseline age, education and genetic risk factors, affect the timing of cognitive decline acceleration. We also assess how different groups of subjects behave on cognitive decline before and after the change point. The model combines a longitudinal mixed effects model with a Cox proportional hazards model connected by a random change point with a log normal distribution. The parameters are estimated by the maximum likelihood method through an ECM algorithm. Compared with joint models with change points developed previously by other authors, our model has several advantages. First, our model uses the semi-parametric Cox model instead of a parametric model for the survival data, therefore is more flexible to different survival distributions. Second, we use the maximum likelihood method and an ECM algorithm to estimate the parameters to avoid the prior assumptions on model parameters. Third, we propose a compromised Fisher information method other than profile likelihood method to obtain a better estimation of the standard errors of the MLEs for model parameters. Finally, the proposed model is successfully implemented to study the preclinical acceleration on the rate of cognitive decline as well as its implication on the risk of developing dementia.
Wednesday October 13, 2010 at 3:00 PM in SEO 636