Data analysis with competing risks and intermediate states/ Ronald B Geskus

Includes Bibliographical references, appendix and index

Basic Concepts -- Competing Risks; Nonparametric Estimation -- Intermediate Events; Nonparametric Estimation -- Regression; Cause-Specific/Transition Hazard -- Regression; Translation to Cumulative Scale -- Epilogue -- Bibliography -- Appendix: Answers to Exercises -- Index

This book 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.