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It is unclear as to how many variables and interactions, given the sample size available, can be reliably assessed with multiple imputation applications.
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Multiple imputation has found many applications in forestry and ecology over the past several years.
The application of multiple imputation is more complex than the use of other missing-data-approaches [ 32].
Missing data were assumed to be missing at random (MAR), thus allowing for application of multiple imputation [ 33].
Recently, there has been an increase in application of multiple imputation and model based methods for missing data in primary analyses.
Survey data will be augmented by quantification of differences to refine alcohol consumption estimates through the application of multiple imputation or inverse probability weighting.
While much has been published on the application of multiple imputation to epidemiological studies, there is limited literature that deals with model building in the presence of missing data, and more specifically model building including interactions.
Therefore, we use Multiple Imputation (MI) to impute the missing union membership information.
Furthermore, the application of standardised multiple imputation techniques rather than simple complete cases analysis is advisable in studies with large amounts of missing data [ 57].
This was imputed using a multivariate multiple imputation procedure with 10 imputations.
This was done for participants eligible at phase 5 and alive at the end of follow-up (n=7412) using the method of multiple imputation by chained equations performed with an SAS-callable software application, IVEware.
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