This book enables the reader to analyse complex time-to-event data himself, using the free open source language R for statistical computing
The data situations considered are competing risks--several, mutually exclusive event types and multistate models, that track an individuals history through different stages over time. These methods are a generalization of the now classical survivalanalysis--the analysis of time to one single event. Such data occur in a variety of fields, including life sciences, social sciences, economics and engineering
The methods are explained on a non-technical level and instantly carried out in R. This book covers data structures, simulating data, analyses of real life data and plotting
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 has contributed several R packages on competing risks and multistate models. Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.
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.
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.
Competing Risks and Multistate Models with R covers models that generalize the analysis of time to a single event (survival analysis) to analyzing 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 non- and semiparametric methods.
This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis is placed on the interpretation of the results. A unique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. This simulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved.
This book is aimed at data analysts, with a background in standard survival analysis, who wish to understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods. The real data examples, R packages, and the entire R code used in the book are available online.
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 has contributed several R packages on competing risks and multistate models. Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.
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.
Competing Risks and Multistate Models with R covers models that generalize the analysis of time to a single event. Both R and multistate methods are promoted with a focus on nonparametric methods.
This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis is placed on the interpretation of the results. A unique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. This simulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved.
This book is aimed at data analysts, with a background in standard survival analysis, who wish to understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods with a focus on non- and semiparametric methods.
The real data examples, R packages, and the entire R code used in the book are available online.
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.
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 has contributed several R packages on competing risks and multistate models. Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.
Über den Autor
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 has contributed several R packages on competing risks and multistate models. Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg. He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.
Inhaltsverzeichnis
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.
Klappentext
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.
This book enables the reader to analyse complex time-to-event data himself, using the free open source language R for statistical computing
The data situations considered are competing risks--several, mutually exclusive event types and multistate models, that track an individuals history through different stages over time. These methods are a generalization of the now classical survivalanalysis--the analysis of time to one single event. Such data occur in a variety of fields, including life sciences, social sciences, economics and engineering
The methods are explained on a non-technical level and instantly carried out in R. This book covers data structures, simulating data, analyses of real life data and plotting
Includes supplementary material: sn.pub/extras
