Sentence examples for multiple imputation generated from inspiring English sources

Assigning missing ethnicity values using multiple imputation generated results very similar to those obtained in our main analysis (Additional file 1: Figure S1).

Because missing data would cause a risk of selection bias as well as loss of power, missing covariate and outcome data were imputed using chained multiple imputation, generating 100 complete data sets (Donders et al. 2006; Sterne et al. 2009).

Multiple imputation generates several realisations of the missing audited data, given the observed data.

Multiple imputation generates m versions of the original data set, with varying missing value replacements in each version and using information from all other variables to generate the replacement.

Using publicly available data, multiple imputation (MI) generated four NHANES biennials (2001 2008) of individual DLC data; we then trended the change over time in each DLC by demographic stratum.

With respect to multiple imputation, we generated 25 iterations and combined the estimates and standard errors using Rubin's Rules (micombine in STATA).

Missing data were handled with multiple imputation to generate 10 datasets which were summarized into one for subsequent analysis.

As a sensitivity analysis, we conducted multiple imputation to generate VL results excluding the individuals who had moved out of the jurisdiction.

The intention is to use multiple imputation to generate complete datasets for analysis.

We used multiple imputation to generate 20 replications to obtain accurate information on the variability in the imputed values (Graham et al. 2007).

To address this, we used multiple imputation to generate 5 complete versions of our dataset, with some variation in the imputed values across versions [ 12, 13].