Gadbury G L, Coffey C S, Allison D B
Department of Mathematics and Statistics, University of Missouri-Rolla, Rolla, MO 65409, USA.
Obes Rev. 2003 Aug;4(3):175-84. doi: 10.1046/j.1467-789x.2003.00109.x.
This paper brings together some modern statistical methods to address the problem of missing data in obesity trials with repeated measurements. Such missing data occur when subjects miss one or more follow-up visits, or drop out early from an obesity trial. A common approach to dealing with missing data because of dropout is 'last observation carried forward' (LOCF). This method, although intuitively appealing, requires restrictive assumptions to produce valid statistical conclusions. We review the need for obesity trials, the assumptions that must be made regarding missing data in such trials, and some modern statistical methods for analysing data containing missing repeated measurements. These modern methods have fewer limitations and less restrictive assumptions than required for LOCF. Moreover, their recent introduction into current releases of statistical software and textbooks makes them more readily available to the applied data analyses.
本文汇集了一些现代统计方法，以解决肥胖症试验中重复测量时出现的缺失数据问题。当受试者错过一次或多次随访，或提前退出肥胖症试验时，就会出现此类缺失数据。处理因退出而导致的缺失数据的常用方法是“末次观察结转”（LOCF）。这种方法虽然直观上很有吸引力，但需要严格的假设才能得出有效的统计结论。我们回顾了肥胖症试验的必要性、此类试验中关于缺失数据必须做出的假设，以及一些用于分析包含重复测量缺失值数据的现代统计方法。这些现代方法比LOCF所需的限制更少，假设也更宽松。此外，它们最近被纳入当前版本的统计软件和教科书中，使得应用数据分析更容易获得这些方法。
