Frailty Models in Survival Analysis

Jones, Byron (Series edited by)/ Chung-Chow, Shein (Series edited by)/ Liu, Jen-pei (Series edited by)/ Peace, Karl E. (Series edited by)/ Wienke, Andreas (Author)

ISBN 10: 1420073885 / ISBN 13: 9781420073881

Published by Chapman & Hall, 2010

About this Item

1st edition. 301 pages. 9.75x6.50x1.00 inches. In Stock.

Seller Inventory # x-1420073885

Bibliographic Details

• Title: Frailty Models in Survival Analysis
• Publisher: Chapman & Hall
• Publication Date: 2010
• Language: English
• ISBN 10: 1420073885
• ISBN 13: 9781420073881
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.76 kilograms

About this title

Synopsis

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models.

The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout.

Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

About the Author

Andreas Wienke is a docent in the Institute of Medical Epidemiology, Biostatistics, and Informatics at Martin-Luther-University Halle-Wittenberg in Germany. In addition to statistical consulting and teaching courses on biostatistics and epidemiology, Dr. Wienke plans, designs, and supervises clinical trials in the University’s Coordination Centre of Clinical Trials.