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Missing Data in Clinical Studies
The Expectation–Maximization Algorithm
monograph
Publication date
(Print):
March 09 2007
Publisher:
John Wiley & Sons, Ltd
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Computer Vision, Deep Learning, Deep Reinforcement Learning, IoT
Author and book information
Book Chapter
Pages
: 93-104
DOI:
10.1002/9780470510445.ch8
SO-VID:
f14a00a9-275a-44c9-9435-44c9c1239848
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Book chapters
pp. i
Front Matter
pp. 1
Introduction
pp. 11
Key Examples
pp. 27
Terminology and Framework
pp. 39
A Perspective on Simple Methods
pp. 55
Analysis of the Orthodontic Growth Data
pp. 67
Analysis of the Depression Trials
pp. 75
The Direct Likelihood Method
pp. 93
The Expectation–Maximization Algorithm
pp. 105
Multiple Imputation
pp. 119
Weighted Estimating Equations
pp. 135
Combining GEE and MI
pp. 145
Likelihood-Based Frequentist Inference
pp. 163
Analysis of the Age-Related Macular Degeneration
pp. 171
Incomplete Data and SAS
pp. 183
Selection Models
pp. 215
Pattern-Mixture Models
pp. 249
Shared-Parameter Models
pp. 253
Protective Estimation
pp. 283
MNAR, MAR, and the Nature of Sensitivity
pp. 313
Sensitivity Happens
pp. 329
Regions of Ignorance and Uncertainty
pp. 353
Local and Global Influence Methods
pp. 417
The Nature of Local Influence
pp. 431
A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data
pp. 451
The Age-Related Macular Degeneration Trial
pp. 461
The Vorozole Study
pp. 483
References
pp. 497
Index
pp. 505
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