Geskus R.B. Data Analysis with Competing Risks and Intermediate States

CRC Press, 2016. — 275 p. — (Chapman & Hall/CRC Biostatistics Series). — ISBN: 1466570350, 9781466570351.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.Basic ConceptsExamples
Data structure
On rates and risks
Non-informative observation schemes?
The examples revisitedBasic techniques from survival analysis
Summary and preview
Exercises
Computer practicals
Competing Risks; Nonparametric EstimationTheoretical relationsR code for classical survival analysis
Estimation based on cause-spefic hazard
Estimation: the subdistribution approach
Standard errors and con_dence 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 HazardRegression 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 individualsExercises
Software
Computer practicals
Regression; Translation to Cumulative ScaleFrom cause-specific/transition hazard to probability
Regression on subdistribution hazard
Multinomial regressionExercises
Software
Computer practicals
Epilogue
Which type of quantity to choose?
Exercises