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We excluded cases with missing variables such as delivery mode or presentation (2%).
This apparent impact of confounding was caused by variables (e.g., maternal age and season of birth) that are weaker risk factors than many missing variables, such as smoking, SES, and weight gain (Berkowitz and Papiernik 1993; Kramer 1987; Lang et al. 1996).
Similar(58)
This is a secondary analysis of an observational trial, and this fact limits our ability to make conclusions, and even though adjustments are made for potential confounders, missed variables such as mechanical ventilation modes and intensity can also contribute to the observed mortality difference between groups.
We assumed that missing variables, including missing outcome variables, were missing at random, such that their values could be predicted by other variables (with known values) in the data set.
Other important missing variables were related to social services, nursing homes, intermediate care, and system-level variables such as integration and coordination between services.
Missing data were noted as such, and no imputation was performed for missing variables.
In such situations, the results after chained equations imputation may systematically differ according to the order in which the missing variables are updated in the chained equations algorithm.
The unknown variables were considered missing variables.
We excluded missing variables from our analyses.
Missing variables for ongoing PTSD was < 1%.
For missing variables, multiple imputation was applied.
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