MI involves the generation of multiple copies of the dataset in each of which missing values are replaced by imputed values sampled from their posterior predictive distribution given the observed data. Multiple imputation (MI) has become a very popular tool for dealing with missing data in recent years. Commonly used analytic approaches exclude patients or records with missing data, which may lead to biased estimates and considerable loss of precision. Analysis of data obtained from such studies is often impeded by the presence of missing data due to item or visit non-response and loss to follow-up. ![]() ![]() ![]() Longitudinal studies, where information on the same participants is obtained repeatedly over time, are frequently used in clinical and population health research.
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