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There were no missing variables, so no imputation was required.
*Multivariate regression analysis drops patients with missing variables, so the final model comprised of 402 instead of 438 patients.
There were no significant differences in demographic or distress variables between the groups with and without missing variables so inclusion should not reduce the internal validity.
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In addition to helping to correct errors in models and missing variables, doing so can help counter potential regressive policy and planning implications by continuing to undercount already marginalized populations.
The unknown variables were considered missing variables.
We excluded missing variables from our analyses.
Secondly, as this was a retrospective study, the ability to avoid missed data is limited, so some patients with more than one missing variable were excluded from the analyses.
Eleven children were missing data for at least one of the study variables, so analyses were conducted using the remaining 474 children will full data.
Presenting details on how MI was carried out may provide insight regarding the distributional assumptions made for variables with missing data, so that the validity of the results can be assessed.
So many variables, so little time.
Missing variables were considered to be missing at random.
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