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In cases of discrepancies or missing data, participants were contacted for further clarification.
To address the missing data, participants (n=3) who did not respond to three or more items (5%) in DICCQ were excluded from further analysis.
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However, this calculation assumed no missing data within participants, no participant withdrawal and equal numbers of participants at each site.
Increased time commitments could create disincentives to participate in research, or lead to greater amounts of missing data because participants do not complete lengthy surveys.
All of these issues are likely to lead to missing data from participants who have poorer PROs and HRQOL outcomes; "informative" missing PRO data.
No missing data or participants lost to follow-up.
After exclusion for missing data, 67 participants were biologically validated as quit and 560 as smokers.
Because of missing data, 19 participants could not be categorized by the UKPDS risk engine.
For studies with a large proportion of missing data, excluding participants with missing data may also reduce the precision of the analysis substantially.
Multiple imputations will be used to impute missing data for participants with less than 10% missing data.
Total energy requirement could not be calculated due to missing data of participants' weight and height in some countries.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com