Competing Risks and Multistate Models with R [electronic resource] /
Beyersmann, Jan.author.
Allignol, Arthur.author.
Schumacher, Martin.author.
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.
Description based upon print version of record.
Includes bibliographical references and index.
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.
Statistics .
R (Computer program language).
Statistical Theory and Methods.