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Missing data categories were used to retain persons who did not have severity information at all levels.
Recognizing concerns raised about potential bias due to the use of missing data categories in regression models (Jones 1996), we repeated our main model as a complete case analysis, omitting participants with missing values for any of the covariates.
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We used two methods for handling the missing data: firstly, we introduced a separate missing data category for each of the incomplete variables, and secondly, we used multiple imputation by chained equations, with the imputation model including all covariates from the main outcome model.
The addition of a missing data category could provide additional insights into the data.
Indicator variables are created for the analysis, including an indicator for the missing data category [ 16].
To account for missing maternal smoking and Townsend quintile data, we included a missing data category for these variables in the regression models.
†The missing data category could not be included in our multivariate models because the numbers of participants in the missing categories were too low.
ICF categories qualified as 'not specified' (8) were recoded as missing data, whereas categories coded as 'not applicable' (9) were recoded as 0 (not impaired, limited or restricted).
aDue to missing data, all categories do not total 504.
For variables with large amounts of missing data, separate categories were created for missing values.
a Due to missing data, all categories do not total 504.
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