Applying mixed regression models to the analysis of repeated-measures data in psychosomatic medicine
Applying mixed regression models to the analysis of repeated-measures data in psychosomatic medicine
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DOI：
10.1097/01.psy.0000239144.91689.ca
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发表时间：
2006-11-01
影响因子：
3.3
通讯作者：
Miller, Gregory E.
中科院分区：
文献类型：
--
作者：
Blackwell, Ekin;de Leon, Carlos F. Mendes;Miller, Gregory E.
目的：虽然重复测量设计在心身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它们的传统统计技术。本文的目标是向读者介绍混合回归模型，它为管理重复测量数据提供了一个更灵活、更准确的框架。方法和结果：我们开始总结混合回归模型在重复测量设计背景下相对于传统统计技术的优势。接下来，我们将为非统计受众概述混合回归模型的概念和数学基础。文章最后以两个例子说明这些模型如何应用于心身研究;一个是对老年人抑郁症状和体重指数变化的前瞻性调查，另一个是对社会交往和皮质醇分泌的日记研究。结论：混合回归模型为分析重复测量数据提供了一种灵活而强大的方法。与传统的研究策略相比，这些模型具有重要的优势，更广泛地应用这些模型可能会提高心身研究的整体质量。
Objective: 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. Methods and Results: 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. Conclusions: 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.