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 exercises at the end of each chapter makes it more useful 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 main part of anzlysis book consists of ten chapters outlining each of the four main approaches to suvival survival analysis: This book is a long-awaited work that summarizes the state of the art of multivariate survival analysis and provides a valuable reference.
These datasets are analysed throughout the text, and results from the various different models presented, interpreted and compared. This book should prove an informative extension to the literature on survival analysis. These would be of most use for those seeking to understand fully the underlying mathematical statistics of these models.
The last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout. 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 suevival 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. The first chapter briefly describes the main features of survival data, and the two main types of multivariate survival data parallel and longitudinal. Every chapter contains an extensive summary which is very helpful Oxford University Press is a department of the University of Oxford.
These chapters contain multivariatw theoretical development, including statistical derivation and issues around estimation of the various models, and survivql more mathematically-orientated than the rest of the book. The description of each dataset is helpfully cross-referenced to the later sections in which the dataset is analysed.
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. Logistic Regression David G. 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 chapter concludes with a summary of the datasets discussed throughout the text, discussing the main questions and which models are used to answer them.
Analysis of Multivariate Survival Data : Philip Hougaard :
Check out the top books of the year on our page Best Books of The chapter summary and bibliographic comments are also very useful. This book should prove an informative extension to the literature on survival analysis. 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 od extensive use made of practical examples.
Circulating vitamin D concentrations and risk of breast and prostate cancer: Analyzing Ecological Data Alain F. The book is a pleasure to read.
Analysis of Multivariate Survival Data
Sign In or Create an Account. Survival Analysis John P. Close mobile search navigation Article navigation. Survival Analysis David G.
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: A chapter describing various measures of bivariate dependence follows.
Analysis of Multivariate Survival Data – Philip Hougaard – Google Books
The aim of the book is very clearly laid down. 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 organization of the book, and the good use of cross referencing, mean that it can be read in varying degrees of depth.
The Best Books of The author’s discussion of time scales, the effect of censoring and the role of covariates touch the very heart of survival analysis. Statistical Methods in Bioinformatics Warren J. Four different approaches to the analysis of such data are presented from an applied point of view.
Email alerts New issue alert. In fact, this book will be most interesting for professional statisticians advancing to this field. Every chapter contains a set of exercises suitable to practice 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.
Looking for beautiful books? There are exercises at the end of each chapter. In addition it is a good reference to the technical literature available in this field.
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.
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. Analysis of Multivariate Survival Data. The example discussed the most often, the Danish twins study, is one which will be of particular relevance to those involved in genetics studies.