Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: A simulation study 01 Mathematical Sciences 0104 Statistics

Abstract

Background: Longitudinal categorical variables are sometimes restricted in terms of how individuals transition between categories over time. For example, with a time-dependent measure of smoking categorised as never-smoker, ex-smoker, and current-smoker, current-smokers or ex-smokers cannot transition to a never-smoker at a subsequent wave. These longitudinal variables often contain missing values, however, there is little guidance on whether these restrictions need to be accommodated when using multiple imputation methods. Multiply imputing such missing values, ignoring the restrictions, could lead to implausible transitions. Methods: We designed a simulation study based on the Longitudinal..