Randomization-Based Inference for Clinical Trials with Missing Outcome Data

Statistics in Biopharmaceutical Research, vol. 16, pp. 456–467

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.

Authors 5

1. Nicole Maria Heussen Aachen Department of Medical Statistics

   RWTH Aachen University

   Affiliation as printed

   Department of Medical Statistics, RWTH Aachen University, Aachen, Germany

2. R.-D Hilgers Aachen Department of Medical Statistics

   RWTH Aachen University

   Affiliation as printed

   Department of Medical Statistics, RWTH Aachen University, Aachen, Germany

3. William Fisher Rosenberger

   George Mason University

   Affiliation as printed

   Department of Statistics, George Mason University, Fairfax, VA

4. Xiao Tan

   George Mason University

   Affiliation as printed

   Department of Statistics, George Mason University, Fairfax, VA

5. Diane Uschner corresponding

   George Washington University

   Affiliation as printed

   Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC