Chapman and Hall/CRC
277 pages | 67 B/W Illus.
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.
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
On rates and risks
Non-informative observation schemes?
The examples revisited
Basic techniques from survival analysis
Summary and preview
R code for classical survival analysis
Competing Risks; Nonparametric Estimation
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
Intermediate Events; Nonparametric Estimation
Introduction; multi-state models
Main concepts and theoretical relations
Example: HIV, SI, AIDS and death
Summary; some alternative approaches
Regression; Cause-Specific/Transition Hazard
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
Regression; Translation to Cumulative Scale
From cause-specific/transition hazard to probability
Regression on subdistribution hazard
Which type of quantity to choose?
Appendix: Answers to Exercises