ANALYSIS OF MULTIVARIATE SURVIVAL DATA HOUGAARD PDF
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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There are exercises at the end of each chapter. His sutvival into the nature of dependence extend far beyond survival analysis and touch some of the most fundamental aspects of our discipline.
Analysis of Multivariate Survival Data : Philip Hougaard :
This dtaa is without any doubt an indispensable reading for both theoretical and practical statisticians. The datasets are described fully in the introduction, and include several examples of each of the more common types of multivariate data.
Sign In or Create an Account. Throughout the book theoretical developments are extensively exemplified by real-life examples and computational aspects are dealt with as well. Looking for beautiful books?
Analysis of Multivariate Survival Data – Philip Hougaard – Google Books
The organization of the book, and the good use of cross referencing, mean that it can be read in varying degrees of depth. This book is, at it fo in the preface, a tool box rather than a cookbook, for those wishing to analyse multivariate survival data.
Product details Format Hardback pages Dimensions x x 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 The exercises at the end of each chapter makes it more useful Home Contact Us Help Free delivery worldwide.
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. Questions to consider before choosing between specific multi-state models, survivla models, marginal models and non-parametric approaches are considered in more detail in four separate tables.
The author’s discussion of time scales, the effect of censoring and the role multivaroate covariates touch the very heart of survival analysis. Analyzing Ecological Data Alain F. Extending the Cox Model Terry Therneau.
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 organization of the book, and the good use of cross-referencing, mean that it can be read in varying degrees of depth.
This book is a long-awaited work that summarizes the state of the art of multivariate survival analysis and provides a valuable reference.
Logistic Regression David G. In the case of the amalysis 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. In my opinion the author has succeeded in completing a valuable monograph on multivariate survival analysis.
I believe this to be the first book on multivariate survival. 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. Review Text From the reviews: Email alerts New issue alert.
Analysis of Multivariate Survival Data
These would be of most use for those seeking to understand fully the underlying mathematical statistics of these models. Every chapter contains a set of exercises suitable to practice There are exercises at the end of each chapter. The book divides into three main sections: The chapter summary and bibliographic comments are also very useful. Every chapter contains an extensive summary which is very helpful A table outlines the limitations of each of the four main approaches.
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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. 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 One of the most useful aspects of this book, in my opinion, is the extensive use made of practical examples.
Code for statistical programs mostly in SAS, with some examples in Splus is given for some of the examples.
It would thus be of most relevance to applied statisticians or epidemiologists requiring a theoretical and practical grounding in the analysis of such data. A practical section hougaadd 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.
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 last chapter provides a very useful summary of the text, with cross-references to the appropriate sections throughout.