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Current superposition methods deal with missing data simply by superpositioning a subset of points that are shared among all the structures.
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Any missing data were simply excluded from the analyses.
This illustrates why missing data cannot simply be ignored; the very fact that some data are missing is informative, and an appropriate analysis must be adopted.
We focused staff efforts on the highest priority data, recognizing that some missing data would simply take too much time to recover.
A typical strategy of dealing with missing data is to simply eliminate any observation that has incomplete data, but this type of "listwise deletion" or "complete case analysis" can result in the lost of many observations and statistical power when the analysis involves multiple variables [ 25].
Those cases with missing data will be simply omitted in the relevant analysis.
7 Increasingly, trialists are expected to justify their handling of missing data and not simply rely on techniques that have been used in other clinical contexts.
It should be noted that the best way to prevent the problems of missing data is to simply avoid it, by employing good data collection techniques and making an effort to chase up missing information.
Moreover, missing data affects study power simply by the reduced availability of data points [45].
Commonly, missing data is managed by simply dropping all cases that are not fully measured.
However, the first approach ignores below detection limit viral load data but simply imputes the missing data with detection limit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com