Missing Data in Clinical Studies

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John Wiley & Sons, Apr 4, 2007 - Medical - 526 pages
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Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.
  • Provides a practical guide to the analysis of clinical trials and related studies with missing data.
  • Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
  • Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
  • Illustrated throughout with real-life case studies and worked examples from clinical trials.
  • Details the use and implementation of the necessary statistical software, primarily SAS.

Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.

 

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Contents

I Preliminaries
1
1 Introduction
3
2 Key Examples
11
3 Terminology and Framework
27
Part II Classical Techniques and the Need for Modelling
39
4 A Perspective on Simple Methods
41
5 Analysis of the Orthodontic Growth Data
55
6 Analysis of the Depression Trials
67
15 Selection Models
185
16 PatternMixture Models
215
17 SharedParameter Models
249
18 Protective Estimation
253
V Sensitivity Analysis
283
19 MNAR MAR and the Nature of Sensitivity
285
20 Sensitivity Happens
313
21 Regions of Ignorance and Uncertainty
329

III Missing at Random and Ignorability
75
7 The Direct Likelihood Method
77
8 The ExpectationMaximization Algorithm
93
9 Multiple Imputation
105
10 Weighted Estimating Equations
119
11 Combining GEE and MI
135
12 LikelihoodBased Frequentist Inference
145
13 Analysis of the AgeRelated Macular Degeneration Trial
163
14 Incomplete Data and SAS
171
IV Missing Not at Random
183
22 Local and Global Influence Methods
353
23 The Nature of Local Influence
417
24 A LatentClass Mixture Model for Incomplete Longitudinal Gaussian Data
431
VI Case Studies
451
25 The AgeRelated Macular Degeneration Trial
453
26 The Vorozole Study
461
References
483
Index
497
Statistics in Practice
505
Copyright

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