Considerations when conducting moderation analysis with a binary outcome: Applications to clinical and social pharmacy research

Abstract

Clinical and social pharmacy researchers often have questions regarding contingencies of effects (i.e., moderation) that are tested by including interactions in statistical models. Much of the available literature for estimating and testing effects that emanate from moderation models is based on extensions of the linear model with continuous outcomes. Binary (or dichotomous) outcome variables, such as prescription-medication misuse versus no misuse, are commonly encountered by clinical and social pharmacy researchers. In moderation analysis, binary outcomes have led to an increased focus on the fact that measures of interaction are scale-dependent; thus, researchers may need to consider both additive interaction and multiplicative interaction. Further complicating interpretation is that the statistical model chosen for an interaction can provide different answers to questions of moderation. This manuscript will: 1) identify research questions in clinical and social pharmacy that necessitate the use of these statistical methods, 2) review statistical models that can be used to estimate effects when the outcome of interest is binary, 3) review basic concepts of moderation, 4) describe the challenges inherent in conducting moderation analysis when modeling binary outcomes, and 5) demonstrate how to conduct such analyses and interpret relevant statistical output (including interpretations of interactions on additive and multiplicative scales with a focus on identifying which statistical models for binary outcomes lead to which measure of interaction). Although much of the basis for this paper comes from research in epidemiology, recognition of these issues has occurred in other disciplines.

Keywords: Additive interaction; Binary outcome; Interaction; Logistic regression; Moderation; Multiplicative interaction.