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As a result of the development of sophisticated techniques, such as multiple imputation, the interest in handling missing data in longitudinal studies has increased enormously in past years.
The standard combining rules for multiple imputation (the repeated imputation rules) have to be modified because the imputations within a nest are correlated, since they share a common set of imputed values for the first set of missing values.
The use of multiple imputation, the role of heteroscedasticity and the possibility of bounding parameter estimates in the presence of non-classical differential measurement error all provide interesting avenues for further research.
However, after multiple imputation, the interpretation of the original results has been confirmed.
After multiple imputation, the sample did not significantly differ from the former sample without multiple imputation.
This result was not, however, consistent: after multiple imputation, the adjusted OR was 0.86 (0.86-1.17).
Similar(42)
For multiple imputations, the imputations are then created by using the regression model and drawing (i.e. simulating) random values from the estimated uncertainty distribution [ 12].
After multiple imputations, the mobility QoL scale remained significant despite its high associated relative increase in variance due to missing data imputation.
Nevertheless, sensitivity analysis using multiple imputation to impute the missing data confirmed the overall findings.
In the multiple imputation analysis, the borderline significant interaction between age and DDE became insignificant (p = 0.3).
Using the monotone logistic aspect of the multiple imputation procedure, we imputed the missing data.
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