About the Book
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.
Table of Contents:
1. Introduction. 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.
About the Author :
Yingwei Peng is Professor of Biostatistics in the Departments of Public Health Sciences and Mathematics and Statistics at Queen’s University and a senior Biostatistician at Queen’s Cancer Research Institute. He has been an Associate Editor of Canadian Journal of Statistics since 2010 and provided referee services to all mainstream statistical journals and Canadian federal funding agencies (NSERC and CIHR). He offered short courses on cure models, either by himself or with Jeremy Taylor (University of Michigan, USA), in Joint Statistical Meetings, ENAR Spring Meeting, and Université catholique de Louvain, Belgium, in 2014. Binbing Yu is an Associate Director in the AstraZeneca oncology biometric group. He has extensive experience in the applications of cure models in public health, clinical trials and health economics and made notable contributions to the development and enhancement of cure modeling for the presentation and analysis of cancer survival data for the USA National Cancer Institute.
Review :
"Overall summary for those looking for a recommendation: outstanding applied statistics book for someone with intermediate knowledge and some data analysis experience, not appropriate for beginners, with just the right balance between theory and practice."
- David Manteigas, International Society for Clinical Biostatistics, 72, 2021
"The book, written by two well-known experts in the field, deals with cure models, wherein a portion of patients are deemed cured after a long period of follow up. This is a very important topic, both statistically and clinically. Though there are several books covering similar topics, the book clearly distinguishes itself from them in the following aspects:
1. It gives a much more comprehensive and updated treatment to cure models, ranging from parametric models to semi-parametric and nonparametric models, from a single endpoint to multivariate outcomes. Undoubtedly, this gives a solid and informative exposure to statisticians who would want to conduct research in the field.
2. It has been extremely helpful that the authors illustrate all the methods in the book by using the software developed by them. Thus, the book contains actionable knowledge that will benefit practitioners.
3. With a number of interesting datasets included in the book, the authors have nicely embedded the models and techniques with them, another practically appealing point.
As such, I strongly recommend the book and believe it will be useful for both theoreticians as well as practitioners."
(Yi Li, University of Michigan, Ann Arbor)
"I’m very glad that a new book on cure models is in preparation. There is an urgent need for a book on this topic…The book is written from a rather applied perspective, focusing on practical estimation, model validation, applications and software, without going more deeply into more theoretical issues like underlying model assumptions to make cure models identifiable, rigorous mathematical statements and properties, etc… The book is clearly written...Moreover, it is self-comprehensive and pleasant to read. It will definitely become an important reference in the field." (Ingrid Van Keilegom, KU Leuven)
"To the best of my knowledge a book on cure models on its own is not available yet. In view of the state of the art of cure models, a comprehensive book on this topic is very pertinent. It could be used as a textbook for a doctoral course in cure models as well as a reference book for researchers in the field." (Ricardo Cao, A Coruña, CITIC, ITMATI)
"The book, written by two well-known experts in the field, deals with cure models, wherein a portion of patients are deemed cured after a long period of follow up. This is a very important topic, both statistically and clinically. Though there are several books covering similar topics, the book clearly distinguishes itself from them in the following aspects:
1. It gives a much more comprehensive and updated treatment to cure models, ranging from parametric models to semi-parametric and nonparametric models, from a single endpoint to multivariate outcomes. Undoubtedly, this gives a solid and informative exposure to statisticians who would want to conduct research in the field.
2. It has been extremely helpful that the authors illustrate all the methods in the book by using the software developed by them. Thus, the book contains actionable knowledge that will benefit practitioners.
3. With a number of interesting datasets included in the book, the authors have nicely embedded the models and techniques with them, another practically appealing point.
As such, I strongly recommend the book and believe it will be useful for both theoreticians as well as practitioners."
(Yi Li, University of Michigan, Ann Arbor)
"I’m very glad that a new book on cure models is in preparation. There is an urgent need for a book on this topic…The book is written from a rather applied perspective, focusing on practical estimation, model validation, applications and software, without going more deeply into more theoretical issues like underlying model assumptions to make cure models identifiable, rigorous mathematical statements and properties, etc… The book is clearly written...Moreover, it is self-comprehensive and pleasant to read. It will definitely become an important reference in the field." (Ingrid Van Keilegom, KU Leuven)
"To the best of my knowledge a book on cure models on its own is not available yet. In view of the state of the art of cure models, a comprehensive book on this topic is very pertinent. It could be used as a textbook for a doctoral course in cure models as well as a reference book for researchers in the field." (Ricardo Cao, A Coruña, CITIC, ITMATI)
"Overall, this book is an admirable compilation of statistical design and methods addressing all phases of oncological drug development. It is primarily targeted at practitioners who will find the illustrative examples utilizing real data helpful. The book presents both Frequentist and Bayesian methods with ample references and useful R libraries, thus allowing readers from many backgrounds to learn about the cure rate model and its application."
Satrajit Roychoudhury, Pfizer USA, Wiley Biometrics, March 2022.
