Teresi J
Hebrew Home for the Aged at Riverdale, New York 10471.
Alzheimer Dis Assoc Disord. 1994;8 Suppl 1:S247-73.
The intent of the methodology section of this volume was to provide an overview of recent thinking about analytic techniques that can be used to study chronic care populations, particularly dementia SCUs. As discussed earlier in this article, although not unique to these populations, longitudinal analysis of chronic care populations frequently involves several problems: nonequivalent comparison groups, unbalanced designs, censoring and attrition, autocorrelation (correlated repeated measures) and heterogeneous correlations across repeated measures. The last 5 years has witnessed considerable change and growth in the development of longitudinal modelling methodologies. Although developments in modelling cut across disciplines, there has been a focus on certain types of methods in several fields of applied statistics: psychometrics (e.g., analysis of change, item response theory, Rasch modelling of binary outcomes), mathematical sociology (e.g., structural equation modelling), demography (e.g., transition modelling), epidemiology (e.g., risk modelling), and biostatistics (e.g., examination of intervention effects using marginal and random effects modelling of repeated measures). An important issue is the interrelationship between measurement and outcome assessment. Source of measurement error must be considered (see Zimmerman and Magaziner in this volume), as must bias in assessments (see Teresi and Golden in this volume). While it has long been known that poor measurement can attenuate and bias estimates of longitudinal effects, the answer is not to attempt artificial solutions through use of corrections for attenuation (unreliability). Design and sampling issues are also critical to the correct conduct of any longitudinal study. Otherwise, estimates of prevalence, incidence, and intervention effects will be in error (see Beckett and Evans, in this volume). Several modelling approaches have been reviewed. Different points of view regarding change analysis have been presented; controversy remains regarding the "best" methods for examining change (see the commentary by Rogosa in this volume). Theory building using methods such as structural equation modelling remains a staple of longitudinal analysis, although several caveats have been discussed. New or revisited methodologies (random effects modelling, of which growth curve analysis can be viewed as a special case and event history modelling) offer promise in dealing with the knotty problems inherent in the longitudinal study of chronic care populations, namely, censoring, attrition, and unbalanced designs resulting in missing data (see the commentary by Nesselroade, in this issue). However, it remains clear that no one model or approach will be optimal for all applications.
本卷方法部分的目的是概述近期关于可用于研究慢性病护理人群，特别是痴呆症特殊护理单元的分析技术的思考。如本文前面所讨论的，虽然这些问题并非这些人群所独有，但慢性病护理人群的纵向分析经常涉及几个问题：不等价对照组、不平衡设计、删失和失访、自相关（相关重复测量）以及重复测量之间的异质性相关。在过去5年中，纵向建模方法有了相当大的变化和发展。尽管建模的发展跨越多个学科，但应用统计学的几个领域都聚焦于某些类型的方法：心理测量学（例如，变化分析、项目反应理论、二元结果的Rasch建模）、数理社会学（例如，结构方程建模）、人口统计学（例如，转变建模）、流行病学（例如，风险建模）以及生物统计学（例如，使用重复测量的边际效应和随机效应建模来检验干预效果）。一个重要问题是测量与结果评估之间的相互关系。必须考虑测量误差的来源（见本卷中齐默尔曼和马加齐纳的文章），评估中的偏差也必须考虑（见本卷中特雷西和戈尔登的文章）。虽然长期以来人们都知道测量不佳会削弱和偏差纵向效应的估计，但答案不是试图通过使用衰减校正（不可靠性）来采取人为解决办法。设计和抽样问题对于任何纵向研究的正确实施也至关重要。否则，患病率、发病率和干预效果的估计将出现误差（见本卷中贝克特和埃文斯的文章）。已经回顾了几种建模方法。已经提出了关于变化分析的不同观点；关于检验变化的“最佳”方法仍存在争议（见本卷中罗戈萨的评论）。使用结构方程建模等方法进行理论构建仍然是纵向分析的主要内容，尽管已经讨论了一些注意事项。新的或重新审视的方法（随机效应建模，其中生长曲线分析可视为一个特例以及事件史建模）有望解决慢性病护理人群纵向研究中固有的棘手问题，即删失、失访以及导致数据缺失的不平衡设计（见本期中内塞尔罗德的评论）。然而，很明显，没有一种模型或方法对所有应用都是最优的。
