부산대학교 도서관

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. [ABSTRACT FROM AUTHOR]