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Twenty-seven patients had at least one missing baseline variable.
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For the final adjusted model, multiple imputation was used to address missing baseline variables and continuous variables were dichotomised to facilitate presentation.
In these analyses, after removal of schools that did not teach at least 11 out of 16 lessons, participants were included for each outcome if they had a follow-up measurement of that outcome; for missing baseline data, an indicator variable was used, which means that for each outcome participants are included even if they do not have a baseline measurement.
In these analyses, participants were included for each outcome if they had a follow-up measurement of that outcome; for missing baseline data, an indicator variable was used, which means that for each outcome participants are included even if they do not have a baseline measurement.
There were no missing data in baseline covariates values, and only a few missing baseline values in outcome variables.
There were no differences between those with and without missing baseline data in terms of sociodemographic and clinical variables.
We impute missing baseline data with the mean values and include dummy variables to the regression to indicate imputation.
We then imputed missing baseline and follow-up scores using all of the variables in the dataset including treatment or control status.
*Analyses with multiple imputation for missing baseline and follow-up data points adjusted for baseline measures of outcome and minimisation variables (practice deprivation and practice size).
We excluded re-admissions and cases with missing baseline data.
Firstly, overall mean scores at baseline substituted missing baseline scores.
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