The Resource Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others]
Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others]
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The item Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others] represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others] represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 This new edition provides a unified, indepth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for rightcensored survival times, repeatedmeasures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the manyshared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. In the second edition, the authors have substantially expanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survival models, within and between effects in longitudinal models, zeroinflated Poisson and negative binomial models, crossvalidation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations; Stata code is also updated. In addition, there are new chapters on methods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables, ¡and on methods for handling missing data, using maximum likelihood, multiple imputation, inverse weighting, and pattern mixture models. From the reviews of the first edition: "This book provides a unified introduction to the regression methods listed in the title ... The methods are well illustrated by data drawn from medical studies ... A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005 "This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow ... I heartily recommend the book" Technometrics, February 2006 "Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this wellunified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
 Language
 eng
 Edition
 2nd ed.
 Extent
 1 online resource (xx, 509 pages).
 Contents

 Strengthening Causal Inference
 Predictor Selection
 Missing Data
 Complex Surveys
 Summary
 Introduction
 Exploratory and Descriptive Methods
 Basic Statistical Methods
 Linear Regression
 Logistic Regression
 Survival Analysis
 Repeated Measures and Longitudinal Data Analysis
 Generalized Linear Models
 Isbn
 9781461413530
 Label
 Regression methods in biostatistics : linear, logistic, survival, and repeated measures models
 Title
 Regression methods in biostatistics
 Title remainder
 linear, logistic, survival, and repeated measures models
 Statement of responsibility
 Eric Vittinghoff [and others]
 Subject

 Biometry
 Biometry
 Biometry
 Biometry  methods
 Biostatistics  methods
 Epidemiology.
 NATURE  Reference
 Public Health/Gesundheitswesen.
 Public health.
 Regression Analysis
 Regression analysis
 Regression analysis
 Regression analysis
 SCIENCE  Life Sciences  Biology
 SCIENCE  Life Sciences  General
 Statistics as Topic  methods
 Statistics for Life Sciences, Medicine, Health Sciences.
 Statistics.
 Language
 eng
 Summary
 This new edition provides a unified, indepth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for rightcensored survival times, repeatedmeasures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the manyshared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. In the second edition, the authors have substantially expanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survival models, within and between effects in longitudinal models, zeroinflated Poisson and negative binomial models, crossvalidation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations; Stata code is also updated. In addition, there are new chapters on methods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables, ¡and on methods for handling missing data, using maximum likelihood, multiple imputation, inverse weighting, and pattern mixture models. From the reviews of the first edition: "This book provides a unified introduction to the regression methods listed in the title ... The methods are well illustrated by data drawn from medical studies ... A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005 "This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow ... I heartily recommend the book" Technometrics, February 2006 "Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this wellunified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
 Cataloging source
 GW5XE
 Dewey number
 570.1/5195
 Index
 index present
 LC call number
 QH323.5
 LC item number
 .R44 2012
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 NLM call number
 WA 950
 http://library.link/vocab/relatedWorkOrContributorName
 Vittinghoff, Eric
 Series statement
 Statistics for biology and health,
 http://library.link/vocab/subjectName

 Biometry
 Regression analysis
 Biometry
 Biostatistics
 Regression Analysis
 Statistics as Topic
 NATURE
 SCIENCE
 SCIENCE
 Biometry
 Regression analysis
 Label
 Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others]
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Strengthening Causal Inference
 Predictor Selection
 Missing Data
 Complex Surveys
 Summary
 Introduction
 Exploratory and Descriptive Methods
 Basic Statistical Methods
 Linear Regression
 Logistic Regression
 Survival Analysis
 Repeated Measures and Longitudinal Data Analysis
 Generalized Linear Models
 Control code
 780441679
 Dimensions
 unknown
 Edition
 2nd ed.
 Extent
 1 online resource (xx, 509 pages).
 File format
 unknown
 Form of item
 online
 Isbn
 9781461413530
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number

 9786613711038
 10.1007/9781461413530
 http://library.link/vocab/ext/overdrive/overdriveId
 371103
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)780441679
 Label
 Regression methods in biostatistics : linear, logistic, survival, and repeated measures models, Eric Vittinghoff [and others]
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Strengthening Causal Inference
 Predictor Selection
 Missing Data
 Complex Surveys
 Summary
 Introduction
 Exploratory and Descriptive Methods
 Basic Statistical Methods
 Linear Regression
 Logistic Regression
 Survival Analysis
 Repeated Measures and Longitudinal Data Analysis
 Generalized Linear Models
 Control code
 780441679
 Dimensions
 unknown
 Edition
 2nd ed.
 Extent
 1 online resource (xx, 509 pages).
 File format
 unknown
 Form of item
 online
 Isbn
 9781461413530
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number

 9786613711038
 10.1007/9781461413530
 http://library.link/vocab/ext/overdrive/overdriveId
 371103
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)780441679
Subject
 Biometry
 Biometry
 Biometry
 Biometry  methods
 Biostatistics  methods
 Epidemiology.
 NATURE  Reference
 Public Health/Gesundheitswesen.
 Public health.
 Regression Analysis
 Regression analysis
 Regression analysis
 Regression analysis
 SCIENCE  Life Sciences  Biology
 SCIENCE  Life Sciences  General
 Statistics as Topic  methods
 Statistics for Life Sciences, Medicine, Health Sciences.
 Statistics.
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