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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Check out the top books of the year on our page Best Books of Analyzing Ecological Data Alain F. 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. His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline. Four different approaches to the analysis of such data are presented from an applied point of view.

There are exercises at the end of each chapter. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide.

Analysis of Multivariate Survival Data

The datasets on length of leukaemia remissions, number of epileptic seizures, exercise test times and competing risks all show types of data which occur in different types of epidemiological study.

In fact, this book will be most interesting for professional statisticians advancing to this field. Several of the exercises suggest hougaarr of specific datasets described in the introduction.

This book extends the field by allowing for multivariate times. 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 various datasets used as examples throughout the hougaaard are then detailed, and the five main aims of multivariate survival analysis presented in a table.


Analysis of Multivariate Survival Data – Philip Hougaard – Google Books

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 Review Text From the reviews: The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout.

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. Description Survival data or more hougaarx time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent.

Receive exclusive offers and updates from Oxford Academic. A chapter describing various measures of bivariate dependence follows. Citing articles via Google Scholar. Clinical Prediction Models Ewout W.

One of the most useful aspects of this book, in my opinion, is the extensive use made of practical multicariate. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples. By using our website you agree to our use of cookies. One of the most useful aspects of this book, in my opinion, is hougaarr extensive use made of practical ex show more.

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. A table outlines the limitations of each of the four main approaches. The chapter summary and bibliographic comments are also very useful. 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. Email alerts New issue alert. Regression Methods in Biostatistics Eric Vittinghoff. The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal.

These datasets are analysed throughout the text, and results from the various different models presented, interpreted and compared.

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. Circulating vitamin D concentrations and risk of multivaariate and prostate cancer: Every chapter contains an extensive summary which is very helpful In addition it is a good reference to the technical literature available in this field. This book should prove an informative extension to the literature on survival analysis.

The organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees of depth. The example discussed the most often, the Danish twins study, is one daha will be of particular relevance to those involved in genetics studies.

In my opinion the author has succeeded in completing a valuable monograph on multivariate survival analysis. The exercises at hoigaard end of each chapter makes it more useful