An Efficient Multiple Imputation Algorithm for Control-Based and Delta-Adjusted Pattern Mixture Models using SAS

ABSTRACT

In clinical trials, mixed effect models for repeated measures (MMRM) and pattern mixture models (PMM) are often used to analyze longitudinal continuous outcomes. We describe a simple missing data imputation algorithm for the MMRM that can be easily implemented in standard statistical software packages such as SAS PROC MI. We explore the relationship of the missing data distribution in the control-based and delta-adjusted PMMs with that in the MMRM, and suggest an efficient imputation algorithm for these PMMs. The unobserved values in PMMs can be imputed by subtracting the mean difference in the posterior predictive distributions of missing data from the imputed values in MMRM. We also suggest a modification of the copy reference imputation procedure to avoid the possibility that after dropout, subjects from the active treatment arm will have better mean response trajectory than subjects who stay on the active treatment. The proposed methods are illustrated by the analysis of an antidepressant trial. Supplementary materials for this article are available online.

KEYWORDS:

• Control-based imputation
• Delta-adjusted imputation
• Missing not at random
• Mixed effects model for repeated measures

Supplementary Materials

The online supplementary materials contain the SAS code.

Acknowledgments

We would like to thank the associate editor and referees for their constructive comments that greatly help to improve the quality of the article.