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Variables were reviewed for missingness.
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We hasten to note that these results are not informative for the case that data are missing at random (MAR), rather than MCAR (see Schafer and Graham 2002, for a comprehensive review on missingness and statistical procedures for handling missing data).
Tests for missingness by genotypic status were also performed to identify errors relating to difficulty calling certain genotypes.
The mechanism for missingness in exposure is ignorable.
The results of this logistic regression model for missingness can inform the plausibility of the different missingness assumptions.
Reasons for missingness may be important and these will be investigated using logistic regression of covariates on an indicator of missingness.
Variables were constructed for each domain and assessed for missingness, collinearity, and normality.
Different methods give potentially different results, because they make different assumptions about the reasons for missingness.
Under this situation, the extended TGLMM accounting for missingness should be preferred.
Any evidence for missingness not completely at random should be presented [ 69].
Data were entered and variables were examined for missingness and outliers.
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