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In general, MI showed good consistency with the complete-case analysis and adequate control over imputation uncertainty.
Estimates from (MI) were expected to show appropriate control over imputation uncertainty, i.e., shrinkage toward zero as γ increases.
It was also chosen over imputation because it was the most conservative way to measure the severity of patients' mental health problems.
The question is not, perhaps, so much, whether AI improves over imputation, but whether imputation analysis improves significantly over AI, given its higher computational (including disc space) and manpower cost.
In (MI*), marker effects are assessed from X ˙ as in (MI), but variability across imputes for estimates of Q and K is also accounted for, through Q ˙ and K ˙, thus allowing for full control over imputation uncertainty in inferences regarding β.
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To prevent over-imputation this procedure was only performed when the missing predictor from the model had a low weight in the equation compared to the other predictors.
Genes that had missing values in more than 1/3 of the samples of a given dataset were excluded from the analysis in order to avoid data over-imputation [ 100].
Imputation-specific predictions may be averaged over imputations using Rubin's rules to give pooled predictions P5 or imputation-specific linear predictors may be averaged over imputations using Rubin's rules and then transformed to give pooled predictions P6.
P1 or its corresponding linear predictor may be averaged over imputations using Rubin's rules to give pooled predictions P2 and P3, respectively.
Imputation-specific predictions may be used to estimate imputation-specific performance measures or which can be subsequently pooled over imputations using Rubin's rules (Rubin, 1987) to give or respectively.
aNumbers in parenthesis are the square root of the variance across imputations divided by the square root of 15, which provides an estimate of the standard deviation of the average over imputations.
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