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We postulate that multiple imputations methods might become an integral part of experimental design as a mean to achieve cost savings in experimental design or to increase the quantity of samples that could be handled for a given cost.
Adjusted for test properties when diabetes by self-report and/or elevated fasting glucose level was the reference standard, the prevalence estimate was 10.8% (10.1 11.5%); multiple imputations methods yielded an estimate of 11.2% (10.4 12.0%).
Moreover, we have not tested multiple imputations methods, which are recommended by several authors [ 8, 15, 17], in order to provide valid inferences for statistical estimates from incomplete data and more stable results.
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Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods.
QTL analysis was performed using R/qtl with the multiple imputations method [ 61].
The multiple imputations method was considered, but this would have changed the scoring algorithm (ibid).
Sensitivity analyses will be conducted, however, using multiple imputations method to explore the potential impact of missing data on outcomes.
Missing data will be imputed using multiple imputation methods if the assumption that missingness is at random seems justifiable.
For the analyses, we used multiple imputation methods to impute all missing values.
In confirmatory analyses, we used multiple imputation methods to impute missing data.
Values were imputed by using multiple imputation methods.
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