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The estimates of variance for the proteins with missing intensities are much closer to the values seen in SSP 1733 when one uses a multiple imputation technique.
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We used a multiple imputation method to impute missing NIHSS values.
Therefore, to evaluate multiple pollutant exposures in one model, we used a multiple imputation approach to impute the missing exposure values (Royston and White 2011).
However, because of missing data, we used a multiple imputation procedure to impute values for the missing data for diabetes and gynecomastia.
Since respondent age was missing for approximately 5% of the study sample, the logistic regression analyses were performed using a multiple imputation approach (10 imputed datasets constructed using Markov Chain Monte Carlo method) with parameter estimates adjusted using Rubin's rules [ 6].
Missing birth weights were imputed using a multiple imputation method [ 20].
Missing data were imputed using a Multiple Imputation by Chained Equation (MICE) approach to obtain a full dataset [ 21].
These values were imputed using a multiple imputation using the aregImpute function from the Hmisc library in R, using additive regression, bootstrapping and predictive mean matching [ 19].
We will examine the structure and pattern of missing data and, if appropriate, will present a sensitivity analysis based on data imputed using a multiple imputation model.
Missing values for prognostic variables were imputed using a multiple imputation by chained equation (MICE) approach (Van Buuren and Groothuis-Oudshoorn, 2011).
Therefore, missing values were imputed using a multiple imputation procedure following the approach of van Buuren and Groothuis-Oudshoorn, 33 resulting in five imputed data sets.
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