Proportional Hazards Regression by O'Quigley, John HC Good

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Características del artículo

Estado
En buen estado: Libro que se ha leído pero que está en buen estado. Daños mínimos en la tapa, ...
Binding
Hardcover
Product Group
Book
Book Title
Proportional Hazards Regression
Weight
1 lbs
IsTextBook
No
ISBN
9780387251486
Categoría

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Product Identifiers

Publisher
Springer New York
ISBN-10
0387251480
ISBN-13
9780387251486
eBay Product ID (ePID)
48259103

Product Key Features

Number of Pages
Xviii, 542 Pages
Publication Name
Proportional Hazards Regression
Language
English
Subject
Biostatistics, Econometrics, Life Sciences / Biology
Publication Year
2008
Type
Textbook
Author
John O'quigley
Subject Area
Science, Business & Economics, Medical
Series
Statistics for Biology and Health Ser.
Format
Hardcover

Dimensions

Item Height
0.5 in
Item Weight
35.4 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2008-920048
Reviews
From the reviews:"The book is clearly intended to be student-friendly. Each chapter begins with a section called Summary and a following one called motivation; each chapter ends with some exercises and class projects. … It is very carefully written, with detailed explanation and discussion everywhere. … I believe that the book can be thoroughly recommended to the student starting his research in the field and to the practitioner who needs to understand some of the theory." (Martin Crowder, International Statistical Review, Vol. 76 (3), 2008), From the reviews: "The book is clearly intended to be student-friendly. Each chapter begins with a section called Summary and a following one called motivation; each chapter ends with some exercises and class projects. ... It is very carefully written, with detailed explanation and discussion everywhere. ... I believe that the book can be thoroughly recommended to the student starting his research in the field and to the practitioner who needs to understand some of the theory." (Martin Crowder, International Statistical Review, Vol. 76 (3), 2008)
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.536
Table Of Content
Background: Probability.- Background: General inference.- Background: Survival analysis.- Marginal survival.- Regression models and subject heterogeneity.- Inference: Estimating equations.- Inference: Functions of Brownian motion.- Inference: Likelihood.- Inference: Stochastic integrals.- Inference: Small samples.- Inference: Changepoint models.- Explained variation.- Explained randomness.- Survival given covariates.- Proofs of theorems, lemmas and corollaries.
Synopsis
Proportional hazards models and their extensions (models with ti- dependent covariates, models with time dependent regression co- cients, models with random coe?cients and any mixture of these) can be used to characterize just about any applied problem to which the techniques of survival analysis are appropriate. This simple obser- tion enables us to ?nd an elegant statistical expression for all plausible practical situations arising in the analysis of survival data. We have a single unifying framework. In consequence, a solid understanding of the framework itself o?ers the statistician the ability to tackle the thorniestofquestionswhichmayarisewhendealingwithsurvivaldata. The main goal of this text is not to present or review the very s- stantial amount of research that has been carried out on proportional hazards and related models. Rather, the goal is to consider the many questions which are of interest in a regression analysis of survival data (prediction, goodness of ?t, model construction, inference and int- pretation in the presence of misspeci?ed models) from the standpoint of the proportional hazards and the non-proportional hazards models., Proportional hazards models and their extensions (models with ti- dependent covariates, models with time dependent regression co- cients, models with random coe'cients and any mixture of these) can be used to characterize just about any applied problem to which the techniques of survival analysis are appropriate. This simple obser- tion enables us to ?nd an elegant statistical expression for all plausible practical situations arising in the analysis of survival data. We have a single unifying framework. In consequence, a solid understanding of the framework itself o'ers the statistician the ability to tackle the thorniestofquestionswhichmayarisewhendealingwithsurvivaldata. The main goal of this text is not to present or review the very s- stantial amount of research that has been carried out on proportional hazards and related models. Rather, the goal is to consider the many questions which are of interest in a regression analysis of survival data (prediction, goodness of ?t, model construction, inference and int- pretation in the presence of misspeci'ed models) from the standpoint of the proportional hazards and the non-proportional hazards models., There are some important, significant departures from much current thinking in the area of proportional hazards regression. Less weight is given to counting processes and martingale theory than is now common. More classical methods of inference are used and while solid theoretically, this is not a mathematical text., This book focuses on the theory and applications of a very broad class of models which underlie modern survival analysis. However, this text differs from most recent works in that it is mostly concerned with methodological issues rather than the analysis itself., The place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models ? proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter ? which underlie modern survival analysis. Unlike other books in this area the emphasis is not on measure theoretic arguments for stochastic integrals and martingales. Instead, while inference based on counting processes and the theory of martingales is covered, much greater weight is placed on more traditional results such as the functional central limit theorem.
LC Classification Number
QH323.5

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