Sentence examples for multiple imputation which from inspiring English sources

n.s.: statistically not significant (p > 0.05) To reduce the possibility of misleading results based on the distribution of missing data, we decided to impute missing values by multiple imputation, which is considered to be the statistically most valid imputation method [ 10].

Additionally, because reduced sample size may result in inefficient estimates and inflated standard error, we used multiple imputation, which was proposed by Rubin in 1987 [ 31], to impute the missing data.

To overcome this bias we used multiple imputation, which allows for the uncertainty about missing data by creating several plausible imputed datasets and appropriately combining their results.

Incomplete data sets are frequently analyzed using multiple imputation, which involves creating multiple complete versions of the data with missing values imputed through random draws from distributions inferred from observed data (2).

13 Here, we focus on multiple imputation, which is a popular alternative to these approaches.

Missing data were handled by single and multiple imputation, which provided consistent results and single imputation is reported herein.

To incorporate performance status and stage, the analysis used multiple imputations, which relied on the assumption that the data were 'missing at random'.

Results for catheter based coronary angiography were imputed for these patients, and were mostly negative (range 97-98.4% across the multiple imputations), which accords with the high negative predictive value of the CT based procedure.

Since complete case analysis can lead to biased estimates and limited power, we addressed the missing data problem by using multiple imputations, which often yield unbiased and more precise estimates if data are missing at random.

27 Missing utility data were imputed using multiple imputation, 28 which avoids bias and enables analysis of the whole sample.

For subjects with missing values on physical activity index (n = 342), smoking status (n = 21), smoking intensity (n = 106), duration of diabetes (n = 410) or education (n = 273), values were imputed with multiple imputation in which 5 duplicate datasets were sampled, with the missing values replaced by imputed values.