Randomization-Based Inference for Clinical Trials with Missing Outcome Data

AbstractRandomization-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 paper 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 setMissing completely at randomMissing at randomRandomization testDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.