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Applied Survival Analysis Regression Modeling of Time to Event Math Statistics
US $9.99
ApproximatelyS$ 13.00
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“Hardcover. Good+ condition. Former owner's name on the inside front cover. No other writing, no ”... Read moreabout condition
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eBay item number:157184438307
Item specifics
- Condition
- Good
- Seller Notes
- Country of Origin
- United States
- Educational Level
- Adult & Further Education
- Personalized
- No
- Level
- Advanced
- Features
- 1st Edition, Illustrated
- ISBN
- 9780471154105
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471154105
ISBN-13
9780471154105
eBay Product ID (ePID)
102842404
Product Key Features
Number of Pages
408 Pages
Publication Name
Applied Survival Analysis : Regression Modeling of Time to Event Data
Language
English
Publication Year
1999
Subject
Probability & Statistics / General, Probability & Statistics / Regression Analysis, General, Diagnosis, Research
Type
Textbook
Subject Area
Mathematics, Medical
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1 in
Item Weight
24 Oz
Item Length
9.4 in
Item Width
6.3 in
Additional Product Features
Edition Number
1
Intended Audience
Scholarly & Professional
LCCN
98-027511
Reviews
"This is actually a great book to read. It has a wealth of examples and applications." (Technometrics, February, 2001) ...the book is an ideal textbook for people with knowledge of regression analysis who want to become acquainted with the methods of survival analysis. (International Journal of Epidemiology, Volume 30 No 2 2001) "...highly recommended..." (Statistical Methods in Medical Research, August 1999) "...the goal of this book is to provide a focused text on regression modeling for the time to event data typically encountered in health related studies...a good description of its contents." (Zentralblatt MATH, Vol. 966, 2001/16)
Dewey Edition
21
Series Volume Number
317
Illustrated
Yes
Dewey Decimal
610/.7/27
Table Of Content
Introduction to Regression Modeling of Survival Data.Descriptive Methods for Survival Data.Regression Models for Survival Data.Interpretation of a Fitted Proportional Hazards Regression Model.Model Development.Assessment of Model Adequacy.Extensions of the Proportional Hazards Model.Parametric Regression Models.Other Models and Topics.Appendices.References.Index.
Synopsis
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include: Variable selection. Identification of the scale of continuous covariates. The role of interactions in the model. Interpretation of a fitted model. Assessment of fit and model assumptions. Regression diagnostics. Recurrent event models, frailty models, and additive models. Commercially available statistical software and getting the most out of it.Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields., This companion to Applied Logistic Regression provides an introduction to the analysis of time-to-event data, with a focus on the health related studies. Emphasis is placed on the modelling of data and the interpretation of results. It approaches the information with attention on the methods, rather than the mathematical theory., A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data. The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data., A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data. The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature. Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include: * Variable selection. * Identification of the scale of continuous covariates. * The role of interactions in the model. * Interpretation of a fitted model. * Assessment of fit and model assumptions. * Regression diagnostics. * Recurrent event models, frailty models, and additive models. * Commercially available statistical software and getting the most out of it. Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
LC Classification Number
R853.S7H67 1999
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