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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Analysis of Multivariate Survival Data

The organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees survlval depth. The book divides into three main sections: The book is a pleasure to read.

These datasets are analysed throughout the text, and eata from the various different models presented, interpreted and compared. The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal.

In my opinion the author has succeeded in completing a valuable monograph hougaarr multivariate survival analysis. Every chapter contains a set of exercises suitable to practice Looking for beautiful books?

The three dependence mechanisms—common events, common risks and event-related dependence—are outlined in a non-mathematical chapter, with a useful table showing common data types relating to these three mechanisms. Clinical Prediction Models Ewout W.


Analysis of Multivariate Survival Data : Philip Hougaard :

There are exercises at the end analysiw each chapter. In fact, this book will be most interesting for professional statisticians advancing to this field. Visit our Beautiful Books page and find lovely books for kids, photography lovers and more.

This book should prove an informative extension to the literature on lf analysis. Statistical Methods in Bioinformatics Warren J. His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline.

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The datasets are described fully in the introduction, and include several examples of each of the more common types of multivariate data. Four different approaches to the analysis of such data are presented from an applied point of view. I think that this book will be useful to statisticians who are dealing with modeling multivariate failure time data in their applied work.

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Extending the Cox Model Terry Therneau. The chapter concludes with a summary of the datasets discussed throughout the text, discussing the main questions and which models are used to answer them. The exercises at the end of each chapter makes it more useful 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.


The organization of the book, and the good use of cross referencing, mean that it can be read in varying degrees of depth. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical ex show more.

The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout. Analyzing Ecological Data Uougaard F. Review Text From the reviews: This book hougard prove an informative extension to the literature on survival analysis. It would thus be of most relevance to applied statisticians or epidemiologists requiring a theoretical and practical grounding in the analysis of such data.

These would be of most use for those seeking to understand fully the underlying mathematical statistics of these models.

Logistic Regression David G.

As the field is rather new, the concepts and the possible types of data are described in detail. Adequate up-to-date references are provided for interested readers to follow up if required. The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed.

Survival Analysis David G.