Missing Data Imputation for a Multivariate Outcome of Mixed Variable Types

Abstract

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.

KEYWORDS:

• Fully conditional specification
• Missing at random
• Piecewise hazard model

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.