Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study
Anurika Priyanjali De Silva, Margarita Moreno-Betancur, Alysha Madhu De Livera, Katherine Jane Lee, Julie Anne Simpson
BMC MEDICAL RESEARCH METHODOLOGY | BMC | Published : 2019
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..View full abstract
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Awarded by National Health and Medical Research Council: a Centre of Research Excellence grant
Awarded by National Health and Medical Research Council
This work was supported by funding from the National Health and Medical Research Council: a Centre of Research Excellence grant, ID 1035261, awarded to the Victorian Centre of Biostatistics (ViCBiostat); and a Senior Research Fellowship ID 1104975 (JAS) and Career Development Fellowship ID 1053609 (KJL). APDS is funded by a Victorian International Research Scholarship and a Melbourne International Fee Remission Scholarship.