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Instead, analysts increasingly favour multiple imputation, where multivariate regression techniques are used to predict missing values on the basis of existing data.
Nonetheless, we advocate multiple imputation where possible because it more comprehensively models imputation uncertainty (among genotypes or other covariates) that could be more pronounced in messier datasets.
Following this assumption, attempts can be made to substitute missing values by using multiple imputation, where a prediction model is used, and therefore accounts for the uncertainty surrounding missing data values.
This approach aims to approximate inferences under a Missing Not At random (MNAR) mechanism by reweighting estimates obtained after multiple imputation where the weights depend on the assumed degree of departure from the MAR assumption.
They decide to use multiple imputation, where the model for imputation assumes lumbar muscle strength is normally distributed with different means but equal variances in the two treatment groups.
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Haplotype uncertainty was taken into account using multiple imputations, where a sample of haplotypes was obtained using Phase 2.1.1 software and the analysis was repeated for each sampled haplotype pair.
This approach may be interpreted as a multiple imputation procedure where many sets of missing observations are generated from their a-posteriori distribution.
This was done through the use of multiple imputation (MI), where a sensitivity analysis was done by conducting a complete-case analysis (CCA).
Multiple imputation (MI), where each missing value is replaced with a set of m (>1) independent values [ 7] to give m separate complete datasets, incorporates uncertainty of the missing data that cannot be achieved with single imputation (m = 1).
We generated 100 multiple imputation data sets where in each data set a subject's Aβ load value was the PIB PET value if available or a simulated PIBcalc value otherwise.
Missing data was imputed using multiple imputation with chained equations (MICE), where appropriate [ 28].
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