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Preventing and Treating Missing Data in Longitudinal Clinical Trials by Craig Mallinckrodt

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13 Analyzing Incomplete Categorical Data

13.1 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 ...

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