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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Review quote From the reviews: 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 muktivariate types relating to these three mechanisms.

Code for statistical programs mostly in SAS, with some examples in Splus is given for some of the examples. As the field is rather new, the concepts and the possible types of data are described in detail.

Visit our Beautiful Books page and find lovely books for kids, photography lovers and more. The book is a pleasure to read. Several of the exercises suggest analyses of specific datasets described in the aurvival. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide.

Analysis of Multivariate Survival Data – Philip Hougaard – Google Books

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. In addition it is a good reference to the technical literature available in this field.

One of the most useful aspects of this book, in my opinion, is the extensive use made of practical ex show more. One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples. There are exercises at the end of each chapter. 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.

Analysis of Multivariate Survival Data. | International Journal of Epidemiology | Oxford Academic

,ultivariate Statistical Methods in Bioinformatics Warren J. Regression Methods in Biostatistics Eric Vittinghoff. It would thus be of most relevance to applied statisticians or epidemiologists requiring a theoretical and practical grounding in the analysis of such data.


The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed. Check out the top books of the year on our page Best Books of Analyzing Ecological Data Alain F. 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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Adequate up-to-date references are provided for interested readers to follow up if required. Oxford University Press is a department of the University of Kultivariate. Poor diet quality in pregnancy is associated with increased risk of excess fetal growth: Survival Analysis David G.

Looking for beautiful books? 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 commendable feature is that each of the chapters starts with an intuitional introduction and ends with a brief summary section, bibliographic comments and exercises.

Analysis of Multivariate Survival Data. Related articles in Google Scholar. The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal. This book extends the field by allowing for multivariate datx. Circulating vitamin D concentrations and risk of breast and prostate cancer: I think that this book will be useful to statisticians who are dealing with modeling multivariate failure time data in their applied work.

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. 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. 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.


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

Four different approaches to the analysis of such data are presented from an applied point of view.

I believe this to be the first book on multivariate survival. One of the most useful aspects of this book, in my opinion, analsyis the extensive use made of practical examples. 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. There are exercises at the end of each chapter. Every chapter contains a set of exercises suitable to practice A chapter describing various measures of bivariate dependence follows.

Citing articles via Google Scholar.

Analysis of Multivariate Survival Data

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. Other books in this series. 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: 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 summary of the theory includes a table outlining questions to consider when identifying the best model to use in a given situation. The exercises at the end of each chapter makes it more useful