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It was decided not to conduct imputations given that there were limited data to do this.
For these missing data, it was not feasible to perform multiple imputations, given that our analyses were computationally expensive (see below).
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We argue that the relevant psychometric literature on PVs, as well as the more general statistical literature on multiple imputations, gives reason for optimism.
Calculating the mean deviation between imputed values and duplicate controls in five datasets, we show that KNN-based imputation gives a systematic bias of the imputed expression values of one-channel depleted spots.
We verified however that imputation gave almost identical values to the alternative procedure of recalculating β-values from the bead-replicate averaged U and M values, thus validating both imputation approaches.
Below 15 MVs, multiple imputation performed very well, while single imputation gave some biased results.
Both complete cases and person-mean imputation gave biased results compared to multiple imputation of the items.
In this study, multiple imputation gives insight into the magnitude of selection bias on the prevalence estimates.
In 15 cases, the imputation gave C/C genotype where the genotyped individuals had C/T genotype.
Parametric imputation gives coverage greater than 95% when β = 0 and much lower – close to 0% in one scenario – with β>0.
It has been shown that a properly performed multiple imputation gives less biased results compared to traditional complete case analysis [ 34].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com