论文部分内容阅读
概述：精神卫生问题的发生、发展及对其管理都需要相对较长的时间，所以相当一部分精神病学研究——特别是能够明确回答有关生物、心理和社会因素复杂的相互作用的研究——要求对患者及其生活环境进行跨时长久的多种评估。然而，许多精神病学的研究人员使用不正确的统计方法来分析这一类型的纵向数据，这一问题会导致分析结果中出现无法识别的偏倚而由此得出不正确的结论。本文就纵向数据分析的话题做了介绍。文章探讨了纵向数据分析中使用的不同数据集结构、缺失数据的分类和处理以及使用纵向数据建立多元回归模型时对个体内相关性校正的方法。“,”Summary:The onset, course, and management of mental health problems typically occur over relatively long periods of time, so a substantial proportion of psychiatric research – particularly the research that can provide clear answers about the complex interaction of biological, psychological, and social factors – requires multiple assessments of individuals and the environments in which they live over time. However, many psychiatric researchers use incorrect statistical methods to analyze this type of longitudinal data, a problem that can result in unrecognized bias in analytic results and, thus, incorrect conclusions. This paper provides an introduction to the topic of longitudinal data analysis. It discusses the different dataset structures used in the analysis of longitudinal data, the classification and management of missing data, and methods of adjusting for intra-individual correlation when developing multivariate regression models using longitudinal data.