2. The Parametric Cure Model
3. The Semiparametric and Nonparametric Cure Models
4. Cure Models for Multivariate Survival Data and Competing Risks
5. Joint Modeling of Longitudinal and Survival Data with a Cure Fraction
6. Testing the Existence of Cured Subjects and Sufficient Follow-up
7. Bayesian Cure Model
8. Analysis of Population-Based Cancer Survival Data
9. Design and Analysis of Cancer Clinical Trials
Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including estimation, inference, and software. This book is useful for statistical researchers and graduate students, and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling, survival models, and R and SAS for data analysis.
The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages, SAS macros, and WinBUGS programs to fit some cure models. The main topics covered include
• the foundation of statistical estimation and inference of cure models for independent and right-censored survival data,
• cure modeling for multivariate, recurrent-event, and competing-risks survival data, and joint modeling with longitudinal data,
• statistical testing for the existence and difference of cure rates and sufficient follow-up,
• new developments in Bayesian cure models,
• applications of cure models in public health research and clinical trials.