Cure Models: Methods, Applications and Implementation - Cure Model

In this review of Cure Models: Methods, Applications and Implementation the bottom line is clear: this is the first modern, systematic textbook in decades that brings together the theory, applied examples and software implementation of statistical cure models. Intended for statisticians, graduate students and applied researchers, the book's greatest strength is its combination of methodological depth and practical guidance, including worked examples and code, which makes it a useful reference for anyone seeking to understand or fit cure model approaches in clinical trials and population studies.

Key Features

• Comprehensive introduction: Covers basic and modern cure model theory so readers gain a structured foundation in estimation and inference.
• Software implementation: Shows how to implement models with R packages, SAS macros and WinBUGS programs to replicate analyses and fit cure models to real data.
• Applied examples: Includes real-world clinical trial and population-based study examples that illustrate model choice and interpretation in practice.
• Targeted prerequisites: Assumes basic knowledge of statistical modeling and survival analysis so readers can move quickly to applied methods.
• Educational value: Serves both as a graduate-level textbook and as a practical reference for researchers seeking appropriate cure model methodology.

Who It's For

This book is best for statistical researchers, graduate students in biostatistics or statistics, and applied practitioners in epidemiology or clinical research who need a thorough, methodical treatment of cure models and practical guidance on software implementation. It is particularly useful when one must decide among cure model types and implement them on real datasets.

Researchers who lack any background in survival analysis or programming in R/SAS may find the prerequisites somewhat steep; beginners in statistics should look for a more introductory survival analysis text before tackling this focused volume.

Pros & Cons

Pros

• Thorough, systematic treatment of modern cure models that connects theory with practice.
• Includes practical code and detailed instructions for R, SAS and WinBUGS to reproduce analyses.
• Real clinical trial and population-study examples make methodological choices tangible.

Cons

• Assumes prior knowledge of survival models and familiarity with R or SAS, which limits accessibility to beginners.

Specifications

Title	Cure Models: Methods, Applications and Implementation
Authors	Binbing Yu, Yingwei Peng
Subject area	Biostatistics / Survival Analysis
Includes software	R packages, SAS macros, WinBUGS programs
Audience	Statistical researchers, graduate students, applied practitioners
Use cases	Clinical trials and population-based studies

Our Verdict

For readers with a grounding in statistical modeling and survival analysis, Cure Models is a valuable, cost-effective reference that fills a long-standing gap by combining rigorous methodology with practical software examples. It is recommended for graduate students and researchers who need to understand, choose and implement cure models in applied settings.

Frequently Asked Questions

Does the book include code to fit cure models?
Yes, it provides detailed instructions and example code for R packages, SAS macros and WinBUGS programs to fit several cure models.

Who should read this book first?
Readers should have basic knowledge of statistical modeling and survival analysis; beginners should study an introductory survival text first.

Are there applied examples?
Yes, the book uses clinical trial and population-based study examples to demonstrate model selection and interpretation.