Abstract
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 analyzing data containing missing repeated measurements. These modern methods have fewer limitations and less restrictive assumptions than required for LOCE. Moreover, their recent introduction into current releases of statistical software and textbooks makes them more readily available to the applied data analyses.
Recommended Citation
G. L. Gadbury et al., "Modern Statistical Methods for Handling Missing Repeated Measurements in Obesity Trial Data: Beyond LOCF," Obesity Reviews, vol. 4, no. 3, pp. 175 - 184, Wiley; Association for the Study of Obesity, Aug 2003.
The definitive version is available at https://doi.org/10.1046/j.1467-789X.2003.00109.x
Department(s)
Mathematics and Statistics
Publication Status
Full Access
Keywords and Phrases
Clinical trial; Ignorable; Imputation; Missing data; Mixed model; Random effects
International Standard Serial Number (ISSN)
1467-7881
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Wiley; Association for the Study of Obesity, All rights reserved.
Publication Date
01 Aug 2003
PubMed ID
12916818
Comments
National Institute of Diabetes and Digestive and Kidney Diseases, Grant P30DK056336