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Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF
Author(s) -
Gadbury G. L.,
Coffey C. S.,
Allison D. B.
Publication year - 2003
Publication title -
obesity reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.845
H-Index - 162
eISSN - 1467-789X
pISSN - 1467-7881
DOI - 10.1046/j.1467-789x.2003.00109.x
Subject(s) - missing data , dropout (neural networks) , computer science , clinical trial , statistical analysis , statistical hypothesis testing , statistics , econometrics , data science , data mining , medicine , machine learning , mathematics , pathology
Summary 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.