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.

Author: Fesho JoJodal
Country: South Africa
Language: English (Spanish)
Genre: Relationship
Published (Last): 26 February 2004
Pages: 264
PDF File Size: 12.27 Mb
ePub File Size: 20.8 Mb
ISBN: 974-4-19217-973-5
Downloads: 16399
Price: Free* [*Free Regsitration Required]
Uploader: Tak

Regression Methods in Biostatistics Eric Vittinghoff. Sign In or Create an Account.

Survival Analysis David G. We use cookies to give you the best possible experience. 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. It furthers the University’s objective of excellence in research, scholarship, and education xata publishing worldwide.

Related articles in Google Scholar. Code for statistical programs mostly in SAS, with some examples in Splus is given for some of the examples. His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline. 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.


Analysis of Multivariate Survival Data

Survival Analysis John P. Extending the Cox Model Terry Therneau. Throughout the book theoretical developments are extensively exemplified by real-life examples and computational aspects are dealt with as well. The summary of the theory includes a table outlining questions to consider when identifying the best model to use in a given situation.

Questions to consider before choosing between specific multi-state models, frailty models, marginal models and non-parametric approaches are considered in more detail in four separate tables.

The chapter summary and bibliographic comments are also very useful. By using our website you agree to our use of cookies.

Analysis of Multivariate Survival Data – Philip Hougaard – Google Books

The organization of the book, and the good use of cross referencing, mean that it can be read in varying degrees of depth. 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 last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout. Four different approaches to the analysis of such data are presented from an applied point of view. durvival

The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed. The level of mathematical detail is nice This book extends the field by allowing for multivariate times.


Analysis of Multivariate Survival Data : Philip Hougaard :

This book is a long-awaited work that summarizes the state of the art suurvival multivariate survival analysis and provides a valuable reference. In my opinion the author has succeeded in completing a valuable monograph on multivariate survival analysis. The main part of the book consists of ten chapters outlining each of the four main approaches to multivariate survival analysis: A table outlines the limitations of each of the four main approaches.

Citing articles via Google Scholar. This book should prove an informative extension to the literature on survival analysis.

Other books in this series. Every chapter contains an extensive summary which is very helpful A chapter summarizing approaches to univariate survival data follows, with indications as to which sections are most important dwta forming the basis for development of the different multivariate models.

Clinical Prediction Models Ewout Skrvival. These would be of most use for those seeking to understand fully the underlying mathematical statistics of these models.

I think that this book will be useful to statisticians who are dealing with modeling multivariate failure time data in their applied work.