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All covariates as well as exposure and outcome variables were included in the imputation process, and 50 imputed data sets were generated.
42 Values for missing data were imputed 10 times using a multiple imputation process with the Mice logarithm (R).
The imputation process was applied to all baseline responders to impute baseline variables using appropriate distributions.
Parallel processing on a multi-core system can make the imputation process substantially faster.
Because the imputed values cannot be treated as actual measured data, the imputation process is usually repeated several times to create multiple complete data sets.
The reasonableness of the imputation process can be judged by comparison of the actual and imputed variables for the injured population.
The imputation process was used to create 10 datasets with missing values replaced by imputed candidates.
This detection and imputation process allows the algorithms that use loop data to perform analysis without requiring them to compensate for missing or incorrect data samples.
This work also considers three different mechanisms governing the distribution of missing values in a dataset, and examines the impact of noise on the imputation process.
If a single model fails to converge, the imputation process as a whole fails.
All variables used in the analyses were included in the imputation process.
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