Yingwei Peng, Binbing Yu

Cure Models (eBook, PDF)

Methods, Applications, and Implementation

• Format: PDF

Produktbeschreibung

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, software. Statistical researchers, graduate students, and practitioners in other disciplines will have a thorough review of modern cure model methodology.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 268
• Erscheinungstermin: 22. März 2021
• Englisch
• ISBN-13: 9780429629686
• Artikelnr.: 61282162

Autorenporträt

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.

Inhaltsangabe

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.