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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Adequate up-to-date references are provided for interested readers hoigaard follow up if required. In addition it is a good reference to the technical literature available in this field. Every chapter contains a set of exercises suitable to practice The Best Books multivaariate The summary of the theory includes a multivqriate outlining questions to consider when identifying the best model to use in a given situation.

The book is a pleasure to read. Regression Methods in Biostatistics Eric Vittinghoff. The organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees of depth.

Analyzing Ecological Data Alain F. 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 This book should prove an informative extension to the literature on survival analysis.

Circulating vitamin D concentrations and risk of breast and prostate cancer: One of the most useful aspects of this book, in my opinion, is the extensive use hougaare of practical ex analysiss more. This book should prove an informative extension to the literature on survival analysis. In fact, this book will be most interesting for professional statisticians advancing to this field.

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

Clinical Prediction Models Ewout W. The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout.

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. Throughout the book theoretical developments are extensively exemplified by real-life examples and computational aspects are dealt with as well.


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

Four different approaches to the analysis of such data are presented from an applied point of view. Other books in this series. In my opinion the author has succeeded in completing a valuable monograph on multivariate survival analysis. 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 datasets are described fully in the introduction, and include several examples of each of the more common types of multivariate data.

Analysis of Multivariate Survival Data

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.

There are exercises at the end of each chapter. 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. 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.

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

One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples. These datasets are analysed throughout the text, and results from the various different models presented, interpreted and compared. We use cookies to give you the best possible experience.

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Product details Format Hardback pages Dimensions x x Statistical Methods in Bioinformatics Warren J. Every chapter contains an extensive summary which is very helpful Visit our Beautiful Books page and find lovely books for kids, photography lovers and more. Review Text From the reviews: The book divides into three main sections: I sutvival this to be the first book on multivariate survival. Review quote From the reviews: As the field hougawrd rather new, the concepts and the possible types of data are described in detail.


Close mobile search navigation Article navigation. The exercises at the end of the more applied chapters relate more ohugaard the identification of sources of bias, dependence mechanisms and time-frames, study design and choice of analysis.

A table outlines the limitations multivarlate each of the four main approaches. 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. Poor diet quality in pregnancy is associated with increased risk of excess fetal growth: The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed.

Analysis of Multivariate Survival Data : Philip Hougaard :

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.

The author’s discussion of time scales, the effect of censoring and the role of covariates touch the very heart of survival analysis. The chapter concludes with a summary of multivaruate datasets discussed throughout the text, discussing the main questions and which models are used to answer them. Citing articles via Google Scholar.

The survivwl datasets used as examples throughout the text are then detailed, and the five main aims of multivariate survival analysis presented in a table.

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. Some of the models in the latter chapters are more complex and less ready for practical use.