Main description:

Focusing on modeling survival data, longitudinal data, and error using the R programming language, this book demonstrates the analysis of data at repeated time points as well as on the occurrence of a related event. Suitable for statisticians, epidemiologists, and medical researchers, it highlights software implementation of both flexible random effect joint models and an alternative transformation model. To enhance understanding, the book includes R programs for implementing the methods and offers examples using publically available data sets and simulated data.

Contents:

Introduction and Background. Motivations for Joint Modeling. Random Effects Models I: Intercept and Slope Models. Random Effects Models II: Flexible Latent Association Models. Transformation Model. Multiple Endpoints: Joint Modeling for Competing Risks. Joint Modeling of Longitudinal Data with Discrete Time Dropout. References. Function Index.