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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.
As nearly 20% of household income data were missing, we used a multiple imputation process [ 27] to obtain 4 imputed values, and then created 4 data sets, each of which contained one of the imputed values.
Since the derivation of a prognostic model can seriously be affected by missing data[ 25], we used a multiple imputation procedure (MICE, Multivariate Imputation by Chained Equations[ 26]) to impute missing values.
We used a multiple imputation measurement error approach to transform CSF Aβ42 into PIB PET units and pool measures of Aβ from either source (Cole et al., 2006).
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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.
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