Sentence examples for avoiding imputation from inspiring English sources
Although four [ 12, 14, 16, 17] and two [ 13, 15] trials lasted up to 24 and 48 weeks, respectively, in part of them, data for efficacy and safety outcomes for periods longer than 12 weeks were not extractable without avoiding imputation analysis.
There are two situations where researchers avoid imputation due to increased error in imputation: (1) SNPs with minor allele frequency less than 1% [1], [6], [10], and (2) association studies where cases and controls are genotyped on different platforms.
Some researchers avoid imputation approaches because of fears of 'making up data'.
However, to avoid imputation of radiographic data, radiographic analyses were based on patients who had available images at baseline/wk104/wk256, comprising ∼66% of randomised patients.
To avoid imputation of genotype calls across the different populations, mpileup was applied within each population separately because the applied method (mpileup) relies in part on Hardy-Weinberg Equilibrium (HWE) for imputation of genotypes [ 51].
Guevara – an Argentinian guerrilla who helped Fidel Castro seize power in Cuba in 1959 – reportedly went to great lengths to avoid imputations of luxury or suggestions that he profited from his position.
For example, imputation accuracy was increased by avoiding the imputation (prediction) of genotypes at loci with low MAF (see Figure 1b).
Identified papers were checked to identify whether results of a single study were published more than once, in order to avoid double imputation.
DIST avoids the imputation of subject-level genotypes by directly applying, to unmeasured SNP statistics, the classical conditional expectation formula for multivariate normal variates.
This complete-case analysis is known to result in loss of accuracy and precision when the data are not missing completely at random [ 32], but was assumed in order to avoid data imputation methods or assumptions about the distribution of the unobserved median OS values.
Using large datasets with minimal population structure for imputation and GS is advocated to avoid heterogeneity of imputation and GS accuracies across subgroups of individuals.
