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The statistical analysis plan will include sensitivity analyses to allow us to fully understand the effect of the assumptions made in any imputation processes used [ 94].
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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).
Multiple imputations are needed to properly account for the uncertainty in the imputation process and the resulting set of imputed estimates can be combined using standard multiple imputation combining rules.
As nearly 20% of household income data were missing, we used a multiple imputation process [ 27] to obtain 4 imputed values, and then created 4 data sets, each of which contained one of the imputed values.
The imputation process was applied to all baseline responders to impute baseline variables using appropriate distributions.
One of its main advantages is that it produces reliable estimates of standard errors, and through the introduction of random error into the imputation process, it considerably reduces potential biases in the imputed data [ 63].
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com