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The proposed imputation technique employs Probabilistic Neural Network (PNN) preceded by mode for imputing the missing categorical data.
Mode substitution was used to impute missing categorical data.
In population pharmacokinetic analyses, missing categorical data are often encountered.
Missing categorical data were replaced by the value for that parameter that corresponded to a normal healthy condition.
Missing categorical data were addressed in the model through the inclusion of an additional level for missing values.
Kappa statistic has been widely used by researchers to evaluate the performance of different imputation techniques on imputing missing categorical data [ 28, 29].
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We evaluated several methods of performing covariate analyses with partially missing categorical covariate data.
Missing categorical or dichotomous variable data were imputed with the mode with missing continuous variables data imputed with the median.
However, no categorical data were missing or erroneous.
To identify behaviour patterns, we used LCA (Lazarsfeld and Henry, 1968; Skrondal and Rabe-Hesketh, 2008), a clustering method that is based on a statistical model and is particularly suited for data collected through questionnaire surveys, because it can appropriately treat categorical data and missing values.
We substituted missing values on all DSM-IV diagnoses with Bayesian multiple imputation (MI), which is the recommended approach with categorical data that are not missing completely at random [ 48].
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