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The data was analysed to assess whether or not specific questions had a high percentage of missing data or the percentage of missing data increased in the last questions, indicating that the questionnaire was too long.
Our dataset had a high percentage of missing data for the components that made up our primary outcome.
However, one major issue with the chief complaint field is a high percentage of missing data for Staten Island hospitals; two of the three Staten Island hospitals currently provide chief complaint data for only 26% of ED visits.
Arrays at 0h time point were left out of the analyses and we also discarded eight arrays with a high percentage of missing data.
Some monitoring stations for benzene were running at the same areas, but these had a high percentage of missing data.
There was a high percentage of missing data for employment, schooling, marital status, and duration of diabetes.
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There was a higher percentage of missing data for secondary outcomes, but no scale had more than 20% missing data.
The SF-6D had a higher percentage of missing data, both at baseline and post-intervention (Table 1).
We repeated this analysis in our real data example with a higher percentage of missing data and obtained the same result.
Compared to Brazier et al. [ 7], SF6D in our study had a higher percentage of missing data at both assessment time points (baseline and 2-year follow up).
The relatively high percentage of missing data in this sample is a major limitation of the study.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com