Data Analysis with Competing Risks and Intermediate States: 1st Edition (Hardback) book cover

Data Analysis with Competing Risks and Intermediate States

1st Edition

By Ronald B. Geskus

Chapman and Hall/CRC

277 pages | 67 B/W Illus.

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pub: 2015-07-14
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Description

Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.

After introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale.

Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The book’s website provides the R code for the computer practicals along with other material.

For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Reviews

I thoroughly recommend the book and am sure that reading it will prompt many young students and researchers to further pursue such models and their applications, possibly embarking on a career in biomedical research.

Carl M. O’Brien, Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, UK

"… a useful read for anyone wanting to apply competing risks or multi-state methods. The examples used throughout the book make the methods clinically meaningful for anyone wanting to simply grasp the concepts behind the methods, and the mathematical theory is rigorously described for those wanting a more in-depth understanding. The book is also supported by a website (http://www.competingrisks.org), which holds additional tips and R code to supplement the exercises at the end of each of the five chapters."

Journal of Biopharmaceutical Statistics, 2015

"This book is excellent for applied statisticians working with time-to-event data."

—James J. Dignam, Department of Public Health Sciences, The University of Chicago

"An accessible introduction to the theory of competing risks and multistate models."

Sandra Eloranta, Karolinska Institutet

"This book is about the particular context of competing risks and intermediate states. These risks or states have often been ignored in survival analysis but the situation is changing rapidly. The book is well written. Basic concepts of survival analysis are recalled and the reader is brought to the most complex concepts. Thus the book can be read both by beginners or experts in survival analysis. The reader can easily skip chapters that are not relevant according to his expertise and usefulness."

International Society for Clinical Biostatistics

Table of Contents

Basic Concepts

Introduction

Examples

Data structure

On rates and risks

Non-informative observation schemes?

The examples revisited

Notation

Basic techniques from survival analysis

Summary and preview

Exercises

R code for classical survival analysis

Computer practicals

Competing Risks; Nonparametric Estimation

Introduction

Theoretical relations

Estimation based on cause-specific hazard

Estimation; the subdistribution approach

Standard errors and confidence intervals

Log-rank tests and other subgroup comparisons

Summary; three principles of interpretability

Exercises

Software

Computer practicals

Intermediate Events; Nonparametric Estimation

Introduction; multi-state models

Main concepts and theoretical relations

Estimation

Example: HIV, SI, AIDS and death

Summary; some alternative approaches

Exercises

Software

Computer practicals

Regression; Cause-Specific/Transition Hazard

Introduction

Regression on cause-specific hazard; basic structure

Combined analysis and type-specific covariables

Why does the stacked approach work?

Multi-state regression models for transition hazards

Example: causes of death in HIV infected individuals

Summary

Exercises

Software

Computer practicals

Regression; Translation to Cumulative Scale

Introduction

From cause-specific/transition hazard to probability

Regression on subdistribution hazard

Multinomial regression

Summary

Exercises

Software

Computer practicals

Epilogue

Which type of quantity to choose?

Exercises

Bibliography

Appendix: Answers to Exercises

Index

About the Author

Ronald B. Geskus is an associate professor at the Academic Medical Center in Amsterdam. He received a Ph.D. in mathematics from the Delft Technical University. His main research interests include competing risks and multi-state models, prediction of events based on time-updated marker values, and causal inference.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED028000
MEDICAL / Epidemiology