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Condition (i) implies correct assumptions regarding the imputation model and the source of missingness in the data.
This reduces three dimensions of missingness to just one timing.
Having determined the pattern of missingness, we next needed to determine the mechanism driving it.
Patterns of missingness were examined and less than 5% of cases had no missing information.
MCAR assumes that the probability of missingness in a variable is independent of any characteristics of the subjects.
Alternative approaches, including using multiple imputation to incorporate uncertainty or using an indicator of missingness, can enhance robustness of evaluations.
The empirical results show that using incomplete cases often increases the effectiveness of nearest neighbor imputation (especially at higher missingness levels), regardless of the type of missingness (i.e., the distribution of missing values in the data).
Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each.
We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve.
The level of missingness was very low for ADNI data at baseline.
However, the proportion of missingness was not the same across these social factors.
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