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The lower the difference between the imputed data and the measured values, the closer the imputation matches known values.
We then calculated the difference between the imputed value for the patient who had the event and the mean of the imputed values for all patients who were on ventilator immediately before the event.
The most notable difference between the imputed and complete case estimates was found for feelings of anxiety (prevalence 25.3%; 95% CI, 22.4 28.5 and prevalence 20.1%; 95% CI, 17.2 23.0 respectively).
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There were no differences between the imputed and non-imputed models describing the associations between SEP score and ST indicators, although the 95% CIs were slightly broader in the unimputed models due to the lower sample size (see online supplementary figure S2).
This confirms the robustness of our findings, since both native Dutch and immigrant survivors have similar response mechanisms from wave 1 to wave 2 and from wave 2 to 3. We could not demonstrate very large differences between the imputed and complete case estimates of the health problems at wave 3.
The effect of imputation was examined, with no qualitative differences observed between the imputed and non-imputed regression models except for one social activity item ('getting along with others').
No significant difference was found between the imputed models and the model shown.
Our results show that functions that estimate LD based on distance only or on the difference in MAF between the imputed SNP and the closest SNP on the lower density marker panel did not provide a good indication of imputation reliability.
The differences between the 10 imputed data sets were small, as only a small percentage of the observations was missing.
Descriptive statistics showed negligible differences between the multiply imputed data sets and the original data (e.g., Ms and Cronbach's alpha values only differed at the second decimal point).
The greatest difference between the observed and imputed data in the final model was for two of the three minority groups whose ethnicity is poorly predicted by name recognition software (Chinese/Other and Mixed).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com