Geskus Data Analysis with Competing Risks and Intermediate States

Erscheinungsjahr 2015
ISBN: 978-1-4665-7036-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 277 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

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.

Zielgruppe

Biostatistics, biomedical, and epidemiological researchers, practitioners, and graduate students.

Autoren/Hrsg.

Geskus, Ronald B.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

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

Über Autor(innen)

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.