Multistate Models for the Analysis of Life History Data

This book will describe a variety of statistical models useful for the analysis of data arising from life history processes. Particular attention will be paid to models useful for the study of chronic diseases to better understand the dynamics of the disease process, the effects of fixed and time-varying covariates, and the use of models for prediction. Observation of the full course of processes for individuals in a study is typically incomplete, so methods of analysis must address this. This book will cover methods for analysis when process data are right -censored, left-truncated or intervalcensored.

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research.  It features:  Discusses a wide range of applications of multistate models, Presents methods for both continuously and intermittently observed life history processes, Gives a thorough discussion of conditionally independent censoring and observation processes, Discusses models with random effects and joint models for two or more multistate processes, Discusses and illustrates software for multistate analysis that is available in R, Target audience includes those engaged in research and applications involving multistate models.