ABSTRACT
The major criticism of simple imputation methods is the underestimation of the variance (see previous chapter). Multiple imputation [125, 126] retains many of the advantages of single imputation but rectifies this problem. Multiple imputation of missing values will be worth the effort only if there is a substantial benefit that cannot be obtained using maximum likelihood methods for the analysis of incomplete data (Chapter 5). Two quotes summarize the issues:
7.2 Overview of multiple imputation
The basic strategy of multiple imputation is to impute 3 to 20 sets of values for the missing data that incorporate both the variability of the HRQoL measure and the uncertainty about the missing observations. Each set of data is then analyzed using complete data methods and the results of the analyses are then combined. The general strategy is summarized in four steps:
Step 1: Selection of the imputation procedure
Although the least technical, selection of an appropriate imputation procedure is the most difficult step. There is a variety of implicit and explicit methods that can be used for multiple imputation [125]. Explicit methods generally utilize regression models, whereas implicit methods utilize sampling techniques. Four specific examples of these strategies are described in the subsequent sections.
