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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This book should prove an informative extension to the literature on survival analysis. By using our website you agree to our use of cookies. Extending the Cox Model Terry Therneau. Throughout the book theoretical developments are extensively exemplified by real-life examples and computational aspects survibal dealt with as well. It would thus be of most relevance to applied statisticians or epidemiologists requiring a theoretical and practical grounding in the analysis of such data.

Analysis of Multivariate Survival Data

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.

This book should prove an informative extension to the literature on survival analysis. Adequate up-to-date references are provided for interested survivall to follow up if required.

Logistic Regression David G. Several of the exercises suggest analyses of specific datasets described in the introduction.

Oxford University Press is a department of the University of Oxford.

Analysis of Multivariate Survival Data – Philip Hougaard – Google Books

This book hougaarc the field by allowing for multivariate times. Survival Analysis David G. The exercises at the end of each chapter makes it more useful His insights into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline. We use cookies to give you the best possible experience.

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

One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples. Review quote From the reviews: The aim of the book is very clearly laid down. Survival Analysis John P. Close mobile search navigation Article navigation. Email alerts New issue alert. This book is, at it states in the preface, a tool box rather than a cookbook, for those wishing to analyse multivariate survival data. A table outlines the limitations of each of the four main approaches.

Other books in this series. 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. The Best Books of 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.

Circulating vitamin D concentrations and risk of breast and prostate cancer: These datasets are analysed throughout the text, and results from the various different models presented, interpreted and compared. There are exercises at the end of each chapter. The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed. 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.

Analysis of Multivariate Survival Data. Poor diet quality in pregnancy is associated with increased risk of excess fetal growth: Product details Multiariate Hardback pages Dimensions x x Regression Methods in Biostatistics Eric Vittinghoff.

As the field is rather new, the concepts and the possible types of data are described in detail. Every chapter contains a set of exercises suitable to practice Clinical Prediction Models Ewout W.

The chapter summary and bibliographic comments are also very useful. In my opinion the author has succeeded in completing a valuable monograph on multivariate survival analysis. Home Contact Us Help Free delivery worldwide. I think that this book will be useful to statisticians who are dealing with modeling multivariate failure time data in their applied work.

The level of mathematical detail is nice The author’s discussion of time scales, the effect of censoring and the role of covariates touch the very heart of survival analysis. Sign In or Create an Account. 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.

Description Survival data or more general 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. A chapter describing various measures of bivariate dependence follows.