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MI: multiple imputation; m: number of imputations; SE: standard error.
As some clinical variables had a substantial number of missing data, we performed multiple imputation (m = 10) using the MICE algorithm [ 22].
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Missing data by unit non-response was handled by Multiple Imputation (MI) (m = 5).
Multiple imputation generates m versions of the original data set, with varying missing value replacements in each version and using information from all other variables to generate the replacement.
Cox PH: Cox proportional hazards; CC: Complete case; MI: Multiple imputation; MAR: Missing at random; K-M: Kaplan-Meier; LRT: Likelihood ratio test; OSHA: Occupational Safety and Health Administration; BIC: Bayesian information criterion.
Multiple imputation was implemented with m imputed datasets under the MAR assumption.
These amounts of missingness were chosen to cover the range of possible missingness in practice [ 30]. 2) Applied the above multiple imputation strategies to generate m = 5 datasets.
In multiple imputation (MI), we create several (m) imputed datasets, in which we, in each set, replace missing observations with random values from a statistical model based on distributions in the observed dataset and underlying assumptions on the nature of the missing data.
For multiple imputation, this procedure is repeated m times.
In general, multiple imputation estimates the missing items M times according to a statistical model.
Multiple imputation yields a set of m complete data matrices of genotype information.
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