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Interviewers were instructed to insist on the screening question about headache history to avoid missing participants with a potential for recurrent severe headaches during their lifetime and particularly during their young adulthood.
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We cannot exclude, however, that we missed participants with undiagnosed or subclinical disease.
Reporting on risk of bias associated with missing participant data was the least problematic item (65%).
Table 3 describes the assessment of the risk of bias associated with missing participant.
As few data were missing, we excluded participants with missing baseline data from all analyses.
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.
To eliminate the influence of missing value replacement, we excluded participants with missing values and performed a statistical analysis of data without replacement.
Because hemoglobin data were significantly missing, our analysis compared the characteristics of participants with missing data vs. other participants.
We excluded 628 pregnant women; 109 participants with missing urine creatinine measurements; 3,179 participants with missing smoking status, pack-years, or serum cotinine; and 229 participants with missing other variables of interest.
We found no substantial differences between subgroups and the total patient population, and no differences between participants with missing values compared with participants with no missing values, except for a few small differences in the CASE IV subgroup.
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CEO of Professional Science Editing for Scientists @ prosciediting.com