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Cure Models

Methods, Applications, and Implementation

; Binbing Yu

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. Les mer
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Vår pris: 1265,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

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.

Fakta

Innholdsfortegnelse

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





Om forfatteren

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 Universite 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.