Cure Models Methods, Applications, and Implementation

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.

1. Introduction A Brief Review of Cure Models Time-to-Event Data and Cured Subjects Survival Models and Cured Models Aim and Scope of the Book 2. The Parametric Cure Model Introduction Parametric Mixture Cure Models Parametric Incidence Submodel Parametric Latency Submodel Parametric PH Latency Submodel Parametric AFT Latency Submodel Other Parametric Latency Submodels Model Estimation Direct Maximization of Observed Likelihood Function Estimation via EM Algorithm Non-Mixture Cure Models Proportional Hazards Cure Model Cure Models Based on Tumor Activation Scheme Cure Models Based on Frailty Models Cure Models Based on Box-Cox Transformation Model Assessment Choosing an Appropriate Parametric Distribution Mixture vs Non-Mixture Cure Models Goodness of Fit by Residuals Software and Applications R Package gfcure R Package mixcure R Package _exsurvcure SAS Macro PSPMCM Summary 3. The Semiparametric and Nonparametric Cure Models Introduction Semiparametric Mixture Cure Models Semiparametric PH Latency Submodel Restrictions on the Upper Tail of the Baseline Distribution Time-Dependent Covariates in the Latency Submodel Semiparametric AFT Latency Submodel Linear Rank Method M-Estimation Method Kernel Smoothing Method Semiparametric AH Latency Submodel Linear Rank Method Kernel Smoothing Method Semiparametric Transformation Latency Submodels Semiparametric Incidence Submodel Semiparametric Spline-Based Cure Models Nonparametric Mixture Cure Models Nonparametric Incidence Submodels Kaplan-Meier Estimator Generalized Kaplan-Meier Estimator Nonparametric Latency Submodels Semiparametric Non-Mixture Cure Models Semiparametric PHC Model General Non-Mixture Cure Models Model Assessment Residuals for Overall Model Fitting Residuals for Latency Submodels Assessing Cure Rate Prediction Concordance Measures for Cure Models Testing Goodness-of-Fit of Parametric Cure Rate Estimation Variable Selection Software and Applications R Package mixcure R Package smcure SAS Macro PSPMCM R Package Survival R Package npcure Summary 4. Cure Models for Multivariate Survival Data and Competing Risks Introduction Marginal Cure Models Marginal Models with Working Independence Marginal Models with Speci_ed Correlation Structures Cure Models with Random E_ects Mixture Cure Models with Frailties Non-Nixture Cure Model with Frailties Cure Models for Recurrent Event Data Cure Models for Competing-Risks Survival Data Classical Approach Vertical Approach Software and Applications R Package geecure R Package intcure Summary 5. Joint Modeling of Longitudinal and Survival Data with a Cure Fraction Introduction Longitudinal and Survival Data with a Cured Fraction Joint Modeling Longitudinal and Survival Data with Shared Random Effects Modeling Longitudinal Proportional Data in Joint Modeling Joint Modeling by Including Longitudinal Effects in Cure Model Applications Summary 6. Testing the Existence of Cured Subjects and Sufficient Follow-up Introduction Tests for Existence of Cured Subjects Without Covariates Likelihood Ratio Test Score Test With Covariates Testing for Sufficient Follow-up Summary 7. Bayesian Cure Model Introduction Flexible Cure Model with Latent Activation Schemes Model Formulation and Inference Bayesian Cure Model with Negative Binomial Distribution Application Bayesian Cure Models with Generalized Modified Weibull Distribution Model Formulation and Inference Application Bayesian Mixture Cure Model with Spatially Correlated Frailties Spatial Mixture Cure Model Application Implementation Summary 8. Analysis of Population-Based Cancer Survival Data Introduction Population-Based Cancer Registry and Survival Data Parametric Cure Models for Net Survival Flexible Parametric Survival Model Flexible Parametric Cure Model Software Implementations Testing the Existence of Statistical Cure Testing Hypothesis of Non-Inferiority of Survival A Minimum Version of One-Sample Log-Rank Test Applications Weibull Mixture Cure Model for Grouped Survival Data Analysis of Individually-Listed Colorectal Cancer Relative Survival Data Testing the Existence of Cure for Colorectal Cancer Patients Summary 9. Design and Analysis of Cancer Clinical Trials Introduction Testing Treatment Effects in the Presence of Cure Comparison of Log-Rank Type Tests Sample Size for the Weighted Log-Rank Test under the Proportional Hazards Cure Model Power and Sample Size in the Presence of Delayed Onset of Treatment Effect and Cure Some Design Issues in Clinical Trials with Cure Cure Modeling in Real-Time Prediction Futility Analysis of Survival Data with Cure Conditional Power for Mixture Cure Models Conditional Power for Non-Mixture Cure Models Application Sample Size Calculation for Trial Design Predicting Future Number of Events Summary

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.