Synopses & Reviews
This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models. Both R and multistate methods are promoted with a focus on nonparametric methods.
Synopsis
Data examples.- An informal introduction to hazard-based analyses.- Competing risks.- Multistate modelling of competing risks.- Nonparametric estimation.- Proportional hazards models.- Nonparametric hypothesis testing.- Further topics in competing risks.- Multistate models and their connection to competing risks.- Nonparametric estimation.- Proportional transition hazards models.- Time-dependent covariates and multistate models.- Further topics in multistate modeling.
Synopsis
Competing Risks and Multistate
Synopsis
This book explains hazard-based analyses of competing risks and multistate data using the R statistical programming code, placing special emphasis on interpretation of results. Includes real data examples, and encourages readers to simulate their own data.
Synopsis
Competing Risks and Multistate Models with R covers models that generalize the analysis of time to a single event.
About the Author
The authors are affiliated with the Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg and the Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany. Jan Beyersmann is Senior Statistician and serves on the editorial board of Statistics in Medicine. Arthur Allignol is Statistician and one of the maintainers of the task view 'Survival Analysis' at the Comprehensive R Archive Network. Martin Schumacher is director of the Institute of Medical Biometry and Medical Informatics, Freiburg.
Table of Contents
Data examples.- An informal introduction to hazard-based analyses.- Competing risks.- Multistate modelling of competing risks.-