Randomization-Based Inference for Clinical Trials with Missing Outcome Data

Abstract

Randomization-based inference is a natural way to analyze data from a clinical trial. But the presence of missing outcome data is problematic: if the data are removed, the randomization distribution is destroyed and randomization tests have no validity. In this article we describe two approaches to imputing values for missing data that preserve the randomization distribution. We then compare these methods to population-based and parametric imputation approaches that are in standard use to compare error rates under both homogeneous and heterogeneous population models. We also describe randomization-based analogs of standard missing data mechanisms and describe a randomization-based procedure to determine if data are missing completely at random. We conclude that randomization-based methods are a reasonable approach to missing data that perform comparably to population-based methods.

Keywords:

• Conditional reference set
• Missing completely at random
• Missing at random
• Randomization test

Supplementary Materials

S1. Type I error rates and power for different imputation methods using the Biased Coin Design.

S2. Results for heterogeneous responses using the Biased Coin Design

Acknowledgments

We thank two anonymous referees for their insightful comments that improved the article.

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

The motivation of this article began during WFRs Fulbright Scholarship at the Institute for Medical Statistics at RWTH Aachen, Germany. The theoretical results in Section 2.1 were derived during a visiting stay of RDH and NH at WFR’s department at George Mason University supported by the IDeAl project funded from the European Union Seventh Framework Programme [FP7 2007-2013] under grant agreement No. 602552. RDH and NH current work is funded as part of the innovation project EVIDENCE-RND by the European Union’s Horizon 2020 Research and Innovation Programme under the EJP RD COFUND-EJP no. 825575.