Request PDF on ResearchGate | Analysis of Multivariate Survival Data | Introduction.- Univariate survival data. Philip Hougaard at Lundbeck. Philip Hougaard. This book is, at it states in the preface, a tool box rather than a cookbook, for those wishing to analyse multivariate survival data. It would thus be. Analysis of Multivariate Survival Data. Philip Hougaard, Springer, New York, No. of pages: xvii+ Price: $ ISBN 0‐‐‐4.
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Sign In or Create an Account. Every chapter contains an extensive summary which is very helpful The exercises at the end of each chapter makes it more useful The main part of the book consists of ten chapters outlining each of the four main approaches to multivariate survival analysis: Various aspects of the theory and statistical inference for each of these approaches are discussed, including helpful sections on assessing goodness-of-fit and choosing between the different models available within each approach.
The book divides into three main sections: Visit our Beautiful Books page and find lovely books for kids, photography lovers and more.
The organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees of depth.
Analysis of Multivariate Survival Data – Philip Hougaard – Google Books
These would be of most use for those seeking to anaysis fully the underlying mathematical statistics of these models. Review Text From the reviews: Throughout the book theoretical developments are extensively exemplified by real-life examples and computational aspects are dealt with as well.
Clinical Prediction Models Ewout W. The datasets on length of leukaemia remissions, number of epileptic houfaard, exercise test times and competing risks all show types of data which occur in different types of epidemiological study.
In the case of the main chapters describing the different approaches, these are theoretically-based, and include examples of deriving transition probabilities for the multi-state model and survivor functions frailty models. The example discussed the most often, the Danish twins study, is one which will be of particular relevance to those involved in genetics studies. It furthers the University’s objective of excellence in survibal, scholarship, and education by publishing worldwide.
This book is a long-awaited work that summarizes the state of the art of multivariate survival analysis and provides a valuable reference. Adequate up-to-date references are provided for interested readers to follow up if required. As the field is rather new, the concepts and the possible types of data are described in detail. The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal.
Other books in this series. Close mobile search navigation Article navigation. Receive exclusive offers and updates from Oxford Academic. Anyone considering writing the second book has a hard act to follow – this sets a very high standard and is recommended for all statisticians with an interest in survival analysis techniques.
Analysis of Multivariate Survival Data. | International Journal of Epidemiology | Oxford Academic
hpugaard A chapter describing various measures of bivariate dependence follows. There are exercises at the end of each chapter. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical ex show more.
Extending the Cox Model Terry Therneau.
The exercises at the end of the more applied chapters relate more to the identification of sources of bias, dependence mechanisms and time-frames, study design and choice of analysis. Houvaard position during pregnancy and DNA methylation signatures at three stages across early life: Review quote From the reviews: In fact, this book will be most interesting for professional statisticians advancing to this field.
The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout. The book is a pleasure to read. The various datasets used as examples throughout the text are then detailed, and the five main aims multivaroate multivariate survival analysis presented in a table. Analywis would thus be of most relevance to applied statisticians or epidemiologists requiring a theoretical and practical grounding in the analysis of such data.
Analysis of Multivariate Survival Data
This book is, at it states in the preface, a tool box rather than a cookbook, for those wishing to analyse multivariate survival data. For some of the datasets, the data are given in the introduction in tabular form, so the reader could attempt to analyse the data and compare the results with those presented.
A practical section on the course of analysis includes tables and discussion of which models are appropriate for which type of data and the relevance of each approach for various purposes. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples.
Email alerts New issue alert. His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline. These datasets are analysed throughout the text, and results from ,ultivariate various different models presented, interpreted and compared.
The author’s discussion of time scales, the effect of censoring and the role of covariates touch the very heart of survival analysis. I think that this book will be useful to statisticians who are dealing with modeling multivariate failure time data in their applied work.
Regression Methods in Biostatistics Eric Vittinghoff. A chapter summarizing approaches to univariate survival data follows, with indications as to which sections are most important as forming the basis for development of the different multivariate models. The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed.
The chapter summary and bibliographic comments are also very useful.