Sentence examples for missing continuous variables from inspiring English sources

Missing continuous variables were imputed in MARS and Random Forest Analyses by replacing missing observations with the median of non-missing cases for each variable: this was done separately for smokers and non-smokers, and for validation and analysis datasets.

Missing categorical or dichotomous variable data were imputed with the mode with missing continuous variables data imputed with the median.

Last observation carried forward imputed values were used for missing continuous variables at week 12. Analysis of covariance, adjusted for the baseline score, was used to compare change from baseline to week 12 between the adalimumab and placebo treatment groups.

The treatment group-specific median or mode was imputed for missing continuous and categorical variables, respectively.

We imputed missing continuous data (n=4) with expectation maximisation and used the median for three variables unrelated to other observed data.

With two exceptions, all variables had less than 5% missing data; therefore, we used single imputation, replacing missing continuous data with means and missing categorical data with modes [ 38].

Missing continuous data were imputed by LOCF.

We presented the final results after imputations in which missing information on continuous variables was replaced by the sample mean while for categorical variables missing data were replaced by modal values.

Data are unlikely to be missing at random; 16 therefore, no attempt will be made to impute numeric missing data, and continuous variables will be categorised with an additional 'missing' category included.

Within the MICE framework, missing values of continuous variables are conventionally imputed by fitting a linear regression model for the observed values, predicting the conditional mean for each missing value, and randomly imputing a value from a normal distribution centered on this conditional mean.

Predictive mean matching (PMM) [ 19] (where missing data are imputed using the observed values with the closest predictive mean from a linear regression model) is another univariate method for imputing missing data for continuous variables, and is less sensitive to violation of the normality assumption than standard linear regression imputation.