Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Acknowledgments
- Preface
- Part I Background and Setting
- Part II Preventing Missing Data
- Part III Analytic Considerations
- Part IV Analyses and The Analytic Road Map
- 9 Analyses of Incomplete Data
- 10 MNAR Analyses
- 11 Choosing Primary Estimands and Analyses
- 12 The Analytic Road Map
- 13 Analyzing Incomplete Categorical Data
- 14 Example
- 15 Putting Principles into Practice
- Bibliography
- Index
13 - Analyzing Incomplete Categorical Data
Published online by Cambridge University Press: 05 February 2013
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Acknowledgments
- Preface
- Part I Background and Setting
- Part II Preventing Missing Data
- Part III Analytic Considerations
- Part IV Analyses and The Analytic Road Map
- 9 Analyses of Incomplete Data
- 10 MNAR Analyses
- 11 Choosing Primary Estimands and Analyses
- 12 The Analytic Road Map
- 13 Analyzing Incomplete Categorical Data
- 14 Example
- 15 Putting Principles into Practice
- Bibliography
- Index
Summary
Introduction
Many of the principles regarding analysis of incomplete data previously discussed for continuous outcomes also apply to categorical outcomes. For example, the missing data mechanisms (Chapter 2) apply to categorical data in essentially the same manner as for continuous data. In addition, considerations regarding modeling time and correlation are also essentially the same as previously outlined for continuous outcomes (Chapter 7). As with continuous data, likelihood-based methods are appealing because of their flexible ignorability properties (Chapter 8). However, their use for categorical outcomes can be problematic because of increased computational requirements as compared with continuous data. Therefore, GEE is a useful alternative.
Despite the similarities between continuous and categorical analyses of incomplete data, some aspects are unique to categorical outcomes, and that is the focus of this chapter. The next section begins with a discussion on marginal and conditional inference because this sets the stage for subsequent sections that discuss the similarities and differences between analyses of continuous and categorical data.
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- Preventing and Treating Missing Data in Longitudinal Clinical TrialsA Practical Guide, pp. 121 - 128Publisher: Cambridge University PressPrint publication year: 2013