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Intention to treat analyses were performed in comparisons between the intervention and comparison group, and missing data was imputed by the multiple imputation procedure in IBM-SPSS statistical package.
Missing data will be imputed using the linear and logistic regression models applied by the multiple imputation functions in SPSS and an intention to treat analysis will be performed.
By Chu and Halloran ([2004]), the estimated VE was 0.74 with 95% confidence interval (0.50,0.88) and estimated VE by the multiple imputation method was 0.71 with 95% confidence interval (0.42,0.86).
This will be addressed by the multiple imputation method.
The missing values will be treated by the multiple imputation method, if necessary.
Such models can be used to make predictions for model performance assessment, but the task is made more complicated by the multiple imputation structure.
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b) We address missing data at single measurements of a repeated measurement study by using the multiple imputation and data augmentation techniques.
Missing data will be completed by multiple imputation using the Multiple Imputation by Chained Equations (MICE) procedure, a technique in which missing values are replaced based on estimated relations in the dataset [ 37].
All other missing values for covariates were imputed using the multiple imputation by chained equations method [ 25].
In the case of data missing at random, the regression techniques described previously were applied after data were imputed using the multiple imputation by chained equations technique to account for random biases of the unobserved covariates.
In this article, we propose the "multiple imputation by ordered monotone blocks" approach, which combines these two basic approaches by decomposing any missingness pattern into a collection of smaller "constructed" monotone missingness patterns, and iterating.
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