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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The three dependence mechanisms—common events, common risks and cata dependence—are outlined in a non-mathematical chapter, with a useful table showing common data types relating to these three mechanisms. Every chapter contains an extensive summary which is dataa helpful I believe this to multivarate the first book on multivariate survival.
Visit our Beautiful Books page and find lovely books for kids, photography lovers and more. The Best Books of The book divides into three main sections: This book should prove an informative extension to the literature on survival analysis.
The aim of multivatiate book is very clearly laid down. A table outlines the limitations of each of the four main approaches. These datasets are analysed throughout the text, and results from the various different models presented, interpreted and compared.
This book is a long-awaited work that summarizes the state of the art of multivariate survival hougaard and provides a valuable reference. 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. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical ex show more.
Some of the models analysks the latter chapters are more complex and less ready for practical use. Review Text From the reviews: Analyzing Ecological Data Alain F. Circulating vitamin D concentrations and risk of breast and prostate cancer: The book is a pleasure to read. Four different approaches to the analysis of such data are presented from an applied point of view.
A chapter describing various measures of bivariate dependence follows.
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 of depth. Survivak organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees of depth. The various datasets used as examples throughout the text are then detailed, and the five main aims of multivariate survival analysis presented in a table.
Review quote From the reviews: The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal. Survival Analysis John P. These chapters contain much theoretical development, including statistical derivation and issues around estimation of the various models, and are more mathematically-orientated than the rest of the book. 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.
Related articles in Google Scholar. His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline.
Receive exclusive offers and updates from Oxford Academic. 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.
Extending the Cox Model Terry Therneau. In addition it is a good reference to the technical literature available in this field. Statistical Methods in Bioinformatics Warren Survivzl. The example discussed the most often, the Danish twins study, is one which will be of particular relevance to those involved in genetics studies. Every chapter contains a set of exercises suitable to practice Email alerts New issue alert.
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Analysis of Multivariate Survival Data : Philip Hougaard :
These would be of most use for those seeking to understand fully the underlying mathematical statistics of these models. Unlike other books on survival, most of which have just one or two chapters dealing with multivariate material, this book is the first comprehensive treatment fully focusing on multivariate survival data It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide.
The author’s discussion of time scales, the effect of censoring and the role of covariates touch the very xurvival of survival analysis. The summary of the hougaardd includes a table outlining questions to consider when identifying the best model to use in a given situation.
The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout. Adequate up-to-date references are provided for interested readers to follow up if required. 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. The chapter concludes analysiz a summary of the datasets discussed throughout surgival text, discussing the main questions and which models are used to answer them.