This low level of pre-existing missing data was assumed to have a negligible effect on the imputation and GS accuracies and for these datasets the original marker data are referred to as version NA0.
eASiMoV (electrical grid Analysis, Simulation, Modeling, and Visualization) Data integrity tests with interactive visual feedback using eTSAnalyzer and eMetaVis in the BReSoC framework (sub-chapter 3.3): overview on existing and missing data in the database and visual representation of value ranges.
After applying existing criteria for missing data analysis, inter-item correlations and endorsement frequencies analysis [ 22], two of these items were removed, giving a scale ranging from 0 to 16 with 0 being the lowest and 16 the highest scores.
Many existing statistical methods for missing data require observations at individual levels.
They have either generated artificial datasets using simulation techniques [ 3], or have made use of existing datasets in which missing data were then artificially created [ 4].
The authors did compare the missing data with the existing data.
Genotype imputation is a crucial step in many genomic studies as all existing genotyping methods result in some missing data.
This manner of presentation should allow for the straightforward modification of existing least-squares superpositioning routines to handle missing data.
In addition, we reanalysed our data using multiple imputation (MI), in which missing data are predicted using existing values from other variables (White et al, 2009).
Women in our study were excluded for the following reasons: pre-existing diabetes, multiple births, and missing data on major items, such as 75 g oral glucose tolerance test (OGTT) results, birth weight, gestational age, delivery mode and whether women with GDM had exercise or dietary intervention during pregnancy.
However, there are limitations in utilising existing sources of data including small sample sizes, missing data and poor data quality.
