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Missing Data in Clinical Studies

ISBN-10: 0470849819

ISBN-13: 9780470849811

Edition: 2006

Authors: Geert Molenberghs, Michael G. Kenward, Michael Kenward

List price: $115.00
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Description:

The first book to concentrate on analysing data from clinical trials, 'Missing Data in Clinical Studies' stakes a practical approach to the subject. Featuring examples and case studies, it includes a chapter on software, with discussion of implementation of the techniques in various different statistical software packages.
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Book details

List price: $115.00
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/16/2007
Binding: Hardcover
Pages: 526
Size: 6.00" wide x 9.00" long x 1.25" tall
Weight: 1.892
Language: English

Preface
Acknowledgements
Preliminaries
Introduction
From Imbalance to the Field of Missing Data Research
Incomplete Data in Clinical Studies
MAR, MNAR, and Sensitivity Analysis
Outline of the Book
Key Examples
Introduction
The Vorozole Study
The Orthodontic Growth Data
Mastitis in Dairy Cattle
The Depression Trials
The Fluvoxamine Trial
The Toenail Data
Age-Related Macular Degeneration Trial
The Analgesic Trial
The Slovenian Public Opinion Survey
Terminology and Framework
Modelling Incompleteness
Terminology
Missing Data Frameworks
Missing Data Mechanisms
Ignorability
Pattern-Mixture Models
Classical Techniques and the Need for Modelling
A Perspective on Simple Methods
Introduction
Simple Methods
Problems with Complete Case Analysis and Last Observation Carried Forward
Using the Available Cases: a Frequentist versus a Likelihood Perspective
Intention to Treat
Concluding Remarks
Analysis of the Orthodontic Growth Data
Introduction and Models
The Original, Complete Data
Direct Likelihood
Comparison of Analyses
Example SAS Code for Multivariate Linear Models
Comparative Power under Different Covariance Structures
Concluding Remarks
Analysis of the Depression Trials
View 1: Longitudinal Analysis
Views 2a and 2b and All versus Two Treatment Arms
Missing at Random and Ignorability
The Direct Likelihood Method
Introduction
Ignorable Analyses in Practice
The Linear Mixed Model
Analysis of the Toenail Data
The Generalized Linear Mixed Model
The Depression Trials
The Analgesic Trial
The Expectation-Maximization Algorithm
Introduction
The Algorithm
Missing Information
Rate of Convergence
EM Acceleration
Calculation of Precision Estimates
A Simple Illustration
Concluding Remarks
Multiple Imputation
Introduction
The Basic Procedure
Theoretical Justification
Inference under Multiple Imputation
Efficiency
Making Proper Imputations
Some Roles for Multiple Imputation
Concluding Remarks
Weighted Estimating Equations
Introduction
Inverse Probability Weighting
Generalized Estimating Equations for Marginal Models
Weighted Generalized Estimating Equations
The Depression Trials
The Analgesic Trial
Double Robustness
Concluding Remarks
Combining GEE and MI
Introduction
Data Generation and Fitting
MI-GEE and MI-Transition
An Asymptotic Simulation Study
Concluding Remarks
Likelihood-Based Frequentist Inference
Introduction
Information and Sampling Distributions
Bivariate Normal Data
Bivariate Binary Data
Implications for Standard Software
Analysis of the Fluvoxamine Trial
The Muscatine Coronary Risk Factor Study
The Crepeau Data
Concluding Remarks
Analysis of the Age-Related Macular Degeneration Trial
Introduction
Direct Likelihood Analysis of the Continuous Outcome
Weighted Generalized Estimating Equations
Direct Likelihood Analysis of the Binary Outcome
Multiple Imputation
Concluding Remarks
Incomplete Data and SAS
Introduction
Complete Case Analysis
Last Observation Carried Forward
Direct Likelihood
Weighted Estimating Equations
Multiple Imputation
Missing Not at Random
Selection Models
Introduction
The Diggle-Kenward