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This is the last in a series of three posts on missing data imputation. In the first post, we reviewed the underlying assumptions and limitations of single imputation methods, in particular, last observation carried forward (LOCF). In the second post, we considered more sophisticated data imputation methods for longitudinal data, Mixed Models for Repeated Measures (MMRM) and Multiple Imputation (MI). An important assumption for both MMRM and MI is that the data are ‘missing at random’ (MAR), if not ‘missing completely at random’ (MCAR). But what if this assumption is not true for all the missing data?
In a previous post on missing data imputation, we reviewed the underlying assumptions and limitations of last observation carried forward (LOCF). This method was widely used in the past because of its straightforward application, ease of understanding, and often incorrect assumption that it is a conservative approach. Over the years, more sophisticated data imputation methods for longitudinal data have been developed that have advantages over the single imputation methods, such as LOCF, baseline observation carried forward (BOCF) or worst observation carried forward (WOCF). We will briefly review two of these methods, mixed models for repeated measures (MMRM) and multiple imputation (MI).