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Of these 161 patients, 10%, 30 %, 50 %, 700 %and90%0 % of causes of death were randomly removed and the variables were imputed with the same imputation model as before.
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Then, the 161 deleted causes of death were imputed using the same imputation model as before.
White et al. (2011) offer another argument in favor of increasing M. Their approach is based on calculating the Monte Carlo error of the results, with the latter defined as the standard deviation across repeated runs of the same imputation procedure with the same data.
However, MaCH and Minimac yielded the same imputation accuracy with 200 reference animals only.
Using almost the same imputation approach (fastPHASE with 30 haplotype clusters), when masked SNP proportions were 20%, 80%, and 95%, the imputation accuracies reported by Weigel et al. (2010) were 0.017, 0.034, and 0.065 higher than those obtained by us, respectively.
For example, even after an impressive ∼20× speed improvement, the genotype imputation using the HRC panel, will still be around 2 times slower than the same imputation using present methods with the 1KG panel (Fuchsberger et al., 2014).
The row IPW/MI* shows the result of IPW/MI with the same misspecified imputation model at stage 2.
In this paper, we specifically consider the case where are multiple imputations from a second imputation procedure with the same variables as the initial imputation approach but with the outcome variable excluded from the imputation models.
There is only one colleague with the same profile so the imputation is simple for this case.
Lastly, Scenario 4 assessed the impact of the number of "anchor" markers genotyped for all subjects, with either 3 markers (Scenario 4.3), 5 markers (Scenario 4.5), or 7 markers (Scenario 4.7) used in the imputation with the same design as Scenario 1.
The trends remain the same for imputation error, figure 4A,B representing the logistic models and random forest models respectively, with MICE coming out second best followed by NN and mean imputation.
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