Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF.
Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF.
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DOI：
10.1046/j.1467-789x.2003.00109.x
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发表时间：
2003-08-01
期刊：
影响因子：
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通讯作者：
Allison, D B
中科院分区：
文献类型：
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作者：
Gadbury, G L;Coffey, C S;Allison, D B
本文汇集了一些现代统计方法来解决重复测量的肥胖试验中数据缺失的问题。当受试者错过一次或多次随访或过早退出肥胖试验时，就会出现这种数据缺失。处理因脱落而缺失数据的常见方法是“末次观察值结转”（LOCF）。这种方法虽然直观上很吸引人，但需要限制性的假设才能得出有效的统计结论。我们回顾了肥胖试验的必要性，在这些试验中必须对缺失数据做出的假设，以及分析包含缺失重复测量数据的一些现代统计方法。与LOCF相比，这些现代方法具有更少的限制和更少的限制性假设。此外，最近将其纳入目前发行的统计软件和教科书，使其更容易用于应用数据分析。
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.