One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types.
Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies.
Also, the approach did not use listwise deletion of cases with missing values, as would be the case with ANOVA.
More generally, our study underscores the importance of reporting the extent of missing values as well as patterns of their distribution, to consider why missing values occurred and their meaning, and to consider the impact of their treatment (e.g., deletion, replacement with zeros) on statistical description and inference [18], [12], [14], [21], [22].
All missing values as well as outliers were omitted from the analysis.
A downward bias is clearly visible with increasing rates of missing values, as well as a decrease in precision.
We describe patterns of missing values as well as comparing those observed and those missing in terms of baseline characteristics.
It predicts missing values as close as possible to the true ones by replacing missing data with probable values based on other available information (26).
The objective of MI is not to predict missing values as close as possible to the true ones but to handle missing data in a way resulting in valid statistical inference (Marchenko, 2011).
On the other hand, the increased use of ultrasound estimates could have reduced the incidence of errors and missing values as well as in the values exceeding 41 weeks [ 34, 35].
Yet one should make such a step dependent on the purpose for which the MacNew is being administered: if it is being employed to evaluate a therapy, one should minimise the production of missing values as far as possible.
