Equivalence and noninferiority testing in regression models and repeated-measures designs

Abstract

Equivalence and noninferiority designs are useful when the superiority of one intervention over another is neither expected nor required. Equivalence trials test whether a difference between groups falls within a prespecified equivalence region, whereas noninferiority trials test whether a preferred intervention is either better or at least not worse than the comparator, with worse being defined a priori. Special designs and analyses are needed because neither of these conclusions can be reached from a nonsignificant test for superiority. Using the data from a companion article, we demonstrate analyses of basic equivalence and noninferiority designs, along with more complex model-based methods. We first give an overview of methods for design and analysis of data from superiority, equivalence, and noninferiority trials, including how to analyze each type of design using linear regression models. We then show how the analogous hypotheses can be tested in a repeated-measures setting in which there are multiple outcomes per subject. We especially address interactions between the repeated factor, usually time, and treatment. Although we focus on the analysis of continuous outcomes, extensions to other data types as well as sample size consideration are discussed.