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Values are often missing in data, for several reasons. Measuring instruments fail, samples are lost or corrupted, patients do not show up to scheduled appointments, and measurements may be deliberately censored if they are known to be untrustworthy above or below certain thresholds. When this happens, it is always necessary to evaluate the nature and the distribution of the gaps, to see whether a remedy must be applied before further analysis of the data. If too many values are missing, or if gaps on one variable occur in association with other variables, ignoring them may invalidate the results of any analysis that is performed. This sort of association is not at all uncommon and may be directly related to the test conditions of the study. For example, when measuring instruments fail, they often do so under conditions of stress, such as high temperature or humidity. As another example, a lot of the missing values in smoking cessation studies occur for those people who begin smoking again and silently withdraw from the study, perhaps out of discouragement or embarrassment.
KeywordsPrimary Biliary Cirrhosis Multiple Imputation Imputation Method Multivariate Normal Distribution Explore Missingness
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