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Objective: Researchers often use model-based multiple imputation to handlemissing at random data to minimize bias while making the best use of allavailable data. However, there are sometimes constraints within the data thatmake model-based imputation difficult and may result in implausible values. Inthese contexts, we describe how to use random hot deck imputation to allow forplausible multiple imputation in longitudinal studies.  Study Design and Setting: We illustrate random hot deck multiple imputationusing The Childhood Health, Activity, and Motor Performance School StudyDenmark (CHAMPS-DK), a prospective cohort study that measured weekly sportsparticipation for 1700 Danish schoolchildren. We matched records with missingdata to several observed records, generated probabilities for matched recordsusing observed data, and sampled from these records based on the probability ofeach occurring. Because imputed values are generated randomly, multiplecomplete datasets can be created and analyzed similar to model-based multipleimputation.  Conclusion: Multiple imputation using random hot deck imputation is analternative method when model-based approaches are infeasible, specificallywhere there are constraints within and between covariates.

Implementing multiple imputation for missing data in longitudinal studies when models are not feasible: A tutorial on the random hot deck approach