While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data.
Because multivariable logistic regression excludes patients who do not have complete data for all variables in the model, we investigated two methods for handling missing data under both models: multiple imputation and replacing missing values with sex specific means.
For all other analyses we substituted missing values with the mean of the resident's non-missing items if a majority of the scale items were not missing.
Josse, J., Chavent, M., Liquet, B. & Husson, F. Handling missing values with regularized iterative multiple correspondence analysis.
Substituting some of the missing values with zeros was observed to have a large impact on the model solution and the computation time.
All patients contain at least 7 missing values, with the most being 48 missing values.
We chose to replace missing values with the average value of the corresponding variable.
Population completeness evaluates missing values with respect to a reference population.
The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the database.
By default AZOrange imputes missing values with the average or the most frequent value of the training set, as implemented by the corresponding Orange method.
Prominent single variable methods are replacing missing values with the available samples' mean, nearest neighbor (NN), linear interpolation and spline (Junninen et al. 2004).
