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Effects will be shown of imputing missing information on the composition and performance of prognostic models, distinguishing imputation of missing values in baseline characteristics and outcome data.
The second step is the single imputation of missing values in background variables which requires an imputation model.
In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data.
The ITT approach is not a remedy for unsound design, and imputation of missing values is not a substitute for complete, good quality data.
SMI and MMI methods may easily be combined with principal component analyses, which can be applied after the imputation of missing values in background variables.
No imputation of missing values was performed.
Using the EM (expectation-maximization) algorithm for imputation of missing values, a second dataset including estimates for individuals with missing data was created (n = 1114).
After imputation of missing values, we first calculated the Normalized Shannon Entropy and the MPR-Statistical Complexity for the each sample.
multiple imputation of missing values.
Imputation of missing values was not performed.
There was no imputation of missing values.
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