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15 Multiple imputations were used to create 20 imputed datasets to address missing covariate data points.
Multiple imputations were used for models where imputed cholesterol was used in creating the weights.
If ≥10 % of data were missing, multiple imputations were performed, assuming they were missing at random [36].
Multiple imputations were used for missing data and the analyses were adjusted for stratification variables (university hospital, shock and hematologic malignancy) and baseline SAPS II and use of plasma.
Multiple imputations were reconciled by creating historical variables.
Data processing and multiple imputations were done in SPSS 17.0.
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Multiple imputations are repeated random draws from the predictive distribution of the missing values.
The Results of multiple imputations are summarized in Table 3.
An assumption in multiple imputations is that of missing at random (MAR) [ 13].
Distribution of stage before and after multiple imputations is presented in Table 2.
Estimates after multiple imputation were more precise than those from a complete case analysis (table 3).
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