Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF

• DOI: 10.1046/j.1467-789x.2003.00109.x
• Publication Date: 2003-07-21T15:29:22Z
• Authors: G. L. Gadbury; C. S. Coffey; D. B. Allison
• Keywords: Dropout (neural networks); Statistical Analysis

Summary This paper brings together some modern statistical methods to address the problem of missing data in obesity trials with repeated measurements. Such occur when subjects miss one or more follow‐up visits, drop out early from an trial. A common approach dealing because dropout is ‘last observation carried forward’ (LOCF). method, although intuitively appealing, requires restrictive assumptions produce valid conclusions. We review need for trials, that must be made regarding such and analysing containing These have fewer limitations less than required LOCF. Moreover, their recent introduction into current releases software textbooks makes them readily available applied analyses.