Department of Family and Community Medicine, University of Texas Health Science Center, San Antonio, Texas 78284, USA.
J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S343-8. doi: 10.1111/j.1532-5415.2010.03053.x.
Longitudinal study designs are indispensable for investigating age-related functional change. There now are well-established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable new applications such as research designs that use modern missing data methods to plan missing data purposefully. This article describes two state-of-the-art statistical methodologies for addressing missing data in longitudinal research: growth curve analysis and statistical measurement models. How the purposeful planning of missing data in research designs can reduce subject burden, improve data quality and statistical power, and manage costs is then described.
纵向研究设计对于研究与年龄相关的功能变化是不可或缺的。现在已经有了成熟的方法来处理纵向研究中的缺失数据。现代缺失数据方法不仅最大限度地减少了与缺失数据相关的大多数问题（例如，降低了功效和有偏的参数估计），而且还有一些有价值的新应用，例如使用现代缺失数据方法有目的地计划缺失数据的研究设计。本文介绍了两种用于处理纵向研究中缺失数据的最先进的统计方法学：增长曲线分析和统计测量模型。然后描述了如何在研究设计中有意规划缺失数据，以减少研究对象的负担、提高数据质量和统计功效，并管理成本。
