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Therefore the focus here is on single imputation methods.
Popular single imputation methods are last observation carried forward and mean imputation.
In contrast to single imputation methods this uncertainty is reflected by the variability of the m results [ 33, 34].
Single imputation methods often lead to biased estimates and under-estimation of the true variability in the data.
An advantage of multiple imputation over single imputation methods is that it allows for the uncertainty of the values that are used to substitute the missing values [ 41].
Moreover, for cluster randomized trials, there is a higher risk that the variance of effect estimate will be underestimated with single imputation methods.
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However, this is not represented in the model if only a single imputation method is used for both groups.
The RMSE differed between the two imputation methods, but only slightly and only for the regression coefficient β2: The RMSE was.037 for the single imputation method and.032 for the nested imputation method.
Linear regression estimate imputation is one of the single imputation method used the surviving creature characteristicswhen the variable with missing value has correlation with explanatory variable (time) and the series of data values follow linear trend [7].
To some extent, the single imputation method mentioned above works well in estimating missing values.
Because race was missing on 65% of the members, we used a single imputation method based on census-based measures of race.
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