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Missing laboratory data is a common issue, but the optimal method of imputation of missing values has not been determined.
This method of imputation of missing values has been shown to yield valid inferences and is recommended for large community intervention trials with small cluster sizes.
We found that the degree of concordance for the three SES measures between adolescent and parent reports decreases with higher family financial stress, and the results hold if treating both parties having missing values has concordant.
The estimation of these missing values has been based on two complementary methodologies which jointly offered feasible and consistent results according to the sample: piecewise cubic Hermite interpolating polynomial (PCHI) and the average rate of change, which was used when PCHI offered unfeasible estimations or out of range results.
Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue.
Similar(55)
The missing values have been multiply imputed using relevant econometric techniques.
Once missing values have been excluded, 1541 observations are used in statistical analysis.
In order to offer comparable results across periods and to not restricting the sample considerably, missing values have been estimated.
In the analysis that follows, missing values have been set to zero, and a dummy variable indicating missing values was included in the analysis.
This reveals that linear spline interpolation estimator for two or more sequentially missing values have smaller error than the linear regression estimator.
Missing values had been replaced by the series' mean (for 8.26% of all values).
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