Bentley John P, Ramachandran Sujith, Salgado Teresa M
Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Faser Hall, University, MS, 38677, USA.
Department of Pharmacy Administration, University of Mississippi School of Pharmacy, Faser Hall, University, MS, 38677, USA.
Res Social Adm Pharm. 2022 Feb;18(2):2276-2282. doi: 10.1016/j.sapharm.2021.04.020. Epub 2021 May 8.
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.
临床和社会药学研究人员常常会对效应的偶然性（即调节作用）存在疑问，这通常通过在统计模型中纳入交互项来进行检验。现有的许多关于估计和检验调节模型产生的效应的文献都是基于具有连续结果的线性模型的扩展。临床和社会药学研究人员经常会遇到二元（或二分）结果变量，比如处方药物滥用与未滥用。在调节分析中，二元结果使得人们更加关注交互作用的测量依赖于量表这一事实；因此，研究人员可能需要同时考虑相加交互作用和相乘交互作用。进一步使解释变得复杂的是，为交互作用选择的统计模型可能会对调节问题给出不同的答案。本手稿将：1）确定临床和社会药学中需要使用这些统计方法的研究问题，2）回顾当感兴趣的结果为二元时可用于估计效应的统计模型，3）回顾调节的基本概念，4）描述对二元结果进行建模时进行调节分析所固有的挑战，5）演示如何进行此类分析并解释相关的统计输出（包括对相加和相乘量表上的交互作用的解释，重点是确定二元结果的哪些统计模型会导致哪种交互作用测量）。虽然本文的许多基础来自流行病学研究，但其他学科也已经认识到了这些问题。
