Chapter 7

Analysis of Incomplete Data

Geert Molenberghs (Universiteit Hasselt and KU Leuven)

Michael G. Kenward

7.1 Introduction

7.2 Case Study

7.3 Data Setting and Methodology

7.4 Simple Methods and MCAR

7.5 Ignorable Likelihood (Direct Likelihood)

7.6 Direct Bayesian Analysis (Ignorable Bayesian Analysis)

7.7 Weighted Generalized Estimating Equations

7.8 Multiple Imputation

7.9 An Overview of Sensitivity Analysis

7.10 Sensitivity Analysis Using Local Influence

7.11 Sensitivity Analysis Based on Multiple Imputation and Pattern-Mixture Models

7.12 Concluding Remarks

7.13 References

More often than not, empirical studies are prone to incompleteness. For about half a century, methods have been developed to address this issue in data analysis. Older ...

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