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We summarized the results into single estimated beta-coefficients for each outcome with confidence intervals and p-values adjusted for the missing data uncertainty [28].
Instead, multiple imputation reflects missing data uncertainty and results in multiple versions of a complete dataset.
The combined estimates incorporate both within- and between-imputation variability, and therefore they reflect missing data uncertainty.
Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty.
Multiple imputation (MI) is one approach to handle the missing covariate data that can properly account for the missing data uncertainty [ 4].
Analysis on the primary outcome (including the imputed data) was furthermore adjusted for missing data uncertainty (using the Stata command 'mi estimate').
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Bayesian networks (BNs) are increasingly being used to model environmental systems, in order to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty.
As any missing data introduce uncertainty, we based our analyses on the currently recommended statistical approach [ 29], which is to impute data rather than to trust the complete-case analyses.
Explicitly, new development are expected to provide ways of filling in missing data and addressing data uncertainty and vagueness using dynamic models of the very systems that these data pertains to.
This technique generates multiple values for respective points of missing data to account for missing-data uncertainty, creating multiple data sets, and introducing between-imputation variance.
The tool provides an easy way to visualize results as well as to identify gaps, missing data and associated uncertainties.
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