  Data collected in clinical trials are often composed of multiple types of
variables. For example, laboratory measurements and vital signs are
longitudinal data of continuous or categorical variables, adverse events may be
recurrent events, and death is a time-to-event variable. Missing data due to
patients' discontinuation from the study or as a result of handling
intercurrent events using a hypothetical strategy almost always occur during
any clinical trial. Imputing these data with mixed types of variables
simultaneously is a challenge that has not been studied extensively. In this
article, we propose using an approximate fully conditional specification to
impute the missing data. Simulation shows the proposed method provides
satisfactory results under the assumption of missing at random. Finally, real
data from a clinical trial evaluating treatments for diabetes are analyzed to
illustrate the potential benefit of the proposed method.
