Blackwell Ekin, de Leon Carlos F Mendes, Miller Gregory E
University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver, BC V6T 1Z4, Canada.
Psychosom Med. 2006 Nov-Dec;68(6):870-8. doi: 10.1097/01.psy.0000239144.91689.ca. Epub 2006 Nov 1.
Although repeated-measures designs are increasingly common in research on psychosomatic medicine, they are not well suited to the conventional statistical techniques that scientists often apply to them. The goal of this article is to introduce readers to mixed regression models, which provide a more flexible and accurate framework for managing repeated-measures data.
We begin with a summary of the advantages that mixed regression models have over conventional statistical techniques in the context of repeated-measures designs. Next, we outline the conceptual and mathematical underpinnings of mixed regression models for a nonstatistical audience. The article ends with two examples of how these models can be applied in psychosomatic research; one deals with a prospective investigation of depressive symptoms and change in body mass index in older adults and the other with a diary study of social interactions and cortisol secretion.
Mixed regression models offer a flexible and powerful approach to analyzing repeated-measures data. They possess important advantages over more traditional strategies, and more widespread application of these models is likely to enhance the overall quality of psychosomatic research.
尽管重复测量设计在身心医学研究中越来越普遍，但它们并不适合科学家经常应用于此类研究的传统统计技术。本文的目的是向读者介绍混合回归模型，该模型为处理重复测量数据提供了一个更灵活、准确的框架。
我们首先总结在重复测量设计背景下，混合回归模型相对于传统统计技术所具有的优势。接下来，我们为非统计学专业的读者概述混合回归模型的概念和数学基础。本文最后给出两个关于这些模型如何应用于身心研究的例子；一个涉及对老年人抑郁症状和体重指数变化的前瞻性调查，另一个是关于社交互动和皮质醇分泌的日记研究。
混合回归模型为分析重复测量数据提供了一种灵活且强大的方法。它们相对于更传统的策略具有重要优势，这些模型的更广泛应用可能会提高身心研究的整体质量。
