Multi-State Survival Models for Interval-Censored Data

"This book introduces Markov models for studying transitions between states over time, when the exact times of transitions are not always observed. Such data are common in medicine, epidemiology, demography, and social sciences research. The multi-state survival modeling framework can be useful for investigating potential associations between covariates and the risk of moving between states and for prediction of multi-state survival processes. The book is appropriate for researchers with a bachelor's or master's degree knowledge of mathematical statistics. No prior knowledge of survival analysis or stochastic processes is assumed. ...
Multi-State Survival Models for Interval-Censored Data serves as a useful starting point for learning about multi-state survival models."
-Li C. Cheung, National Cancer Institute, in the Journal of the American Statistical Association, January 2018

"This book aims to provide an overview of the key issues in multistate models, conduct and analysis of models with interval censoring. Applications of the book concern on longitudinal data and most of them are subject to interval censoring. The book contains theoretical and applicable examples of different multistate models. ... In summary, this book contains an excellent theoretical coverage of multistate models concepts and different methods with practical examples and codes, and deals with other topics relevant this kind of modelling in a comprehensive but summarised way."
- Morteza Hajihosseini, ISCB News, May 2017

"This is the first book that I know of devoted to multi-state models for intermittently-observed data. Even though this is a common situation in medical and social statistics, these methods have only previously been covered in scattered papers, software manuals and book chapters. The level is approximately suitable for a postgraduate statistics student or applied statistician. The structure is clear, gradually building up