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In the first analysis, dummy variables were used for missing data, with creation of a categorical indicator for missing responses (missing category) (see online supplementary appendix table S1).
For most items included in both indices, there was less than 5percentt missing data (with ≥ 80% of items having no missing values) [ 17].
We compared popular methods to handle missing data with multiple imputation (a more sophisticated method that preserves data).
However, the first approach ignores below detection limit viral load data but simply imputes the missing data with detection limit.
For all primary and secondary outcome measures, sensitivity analyses were performed by replacing missing data with the last value.
The shadow and highlight pixels are considered as missing data, with the objective of reducing their influence on the result.
Consequentially, studies conducted in order to investigate the acute effect of air pollution should fill in missing data with the K-SVD method.
These intervention effects were also less robust to missing data with the sensitivity analysis suggesting a trend towards non-significance (Additional file 1: Table S6b).
They consider the shadow and highlight pixels as missing data, with the objective of reducing the influence of these pixels on the final result.
Fourteen (45%) studies reported automated and/or manual editing of the raw ECG to remove artefact by replacing the missing data with cubic spline or linear interpolation methods.
We have been able to include several important confounders and have imputed the missing data, with largely similar results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com