Joint Modeling of Longitudinal and Time-to-Event Data (ADDED FEE) — Professional Development Continuing Education Course
ASA, Biometrics Section
Longitudinal analysis and time-to-event data analysis are among the fastest expanding areas of statistics and biostatistics in the past three decades. In recent years, these two seemingly different areas of statistics have crossed with the rapidly growing interest in development of joint models for longitudinal and time-to-event data to address challenging issues that cannot be properly handled using standard methods within each area. This course aims to a give a systematic introduction and review of state-of-the-art statistical methodology developed in recent years for joint models. We will provide motivating examples and an overview of statistical modeling and concepts that are fundamental to understand joint models, discuss several main areas in which joint models have been developed to address important scientific questions and issues, including non-ignorable missing data in longitudinal analysis, event time models with intermittently measured time-dependent covariates, longitudinal studies with informative observation times, joint models for competing risks event times, and some further topics. The last section will give the audience hands-on experience of analyzing data using joint models. Examples will be illustrated by computer programs in R. The course concludes with a self-practice session.
Instructor(s): Robert Elashoff, UCLA; Gang Li, UCLA; Ning Li, UCLA