Advanced Survival Models

By: Catherine Legrand

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Hardcover | 23 March 2021 | Edition Number 1

At a Glance

Hardcover
362 Pages

Dimensions(cm)
23.39 x 15.6 x 2.06

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Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome.

Features

Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome

Uses consistent notation throughout the book for the different techniques presented

Explains in which situation each of these models should be used, and how they are linked to specific research questions

Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians

Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets

This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

Industry Reviews

"The book provides, in a single reference, today's status of survival data modeling. Emphasis is on implementation and interpretation of the output of such models. The narrative and the technical style are nicely in balance, making the reading light and pleasant. The book includes a number of data sets, mainly from oncology, that are used to demonstrate the methodology via case studies, including details on the R and SAS programming. All chapters include a 'further reading' section, with important references for deeper digging into the subject. I highly recommended the text, not only for the applied statisticians working with time-to-event data but also for statisticians looking for a comprehensive single reference that provides an excellent overview of advanced survival modeling."
- Paul Janssen, Hasselt University

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