Journal article
Bias in the last observation carried forward method under informative dropout
Journal of statistical planning and inference, Vol.139(2), pp.246-255
02/01/2009
DOI: 10.1016/j.jspi.2008.04.017
Abstract
Subject dropout is an inevitable problem in longitudinal studies. It makes the analysis challenging when the main interest is the change in outcome from baseline to endpoint of study. The last observation carried forward (LOCF) method is a very common approach for handling this problem. It assumes that the last measured outcome is frozen in time after the point of dropout, an unrealistic assumption given any time trends. Though existence and direction of the bias can sometimes be anticipated, the more important statistical question involves the actual magnitude of the bias and this requires computation. This paper provides explicit expressions for the exact bias in the LOCF estimates of mean change and its variance when the longitudinal data follow a linear mixed-effects model with linear time trajectories. General dropout patterns are considered that may depend on treatment group, subject-specific trajectories and follow different time to dropout distributions. In our case studies, the magnitude of bias for mean change estimators linearly increases as time to dropout decreases. The bias depends heavily on the dropout interval. The variance term is always underestimated. (C) 2008 Elsevier B.V. All rights reserved.
Details
- Title: Subtitle
- Bias in the last observation carried forward method under informative dropout
- Creators
- Chandan Saha - Indiana UniversityMichael P. Jones - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of statistical planning and inference, Vol.139(2), pp.246-255
- Publisher
- Elsevier
- DOI
- 10.1016/j.jspi.2008.04.017
- ISSN
- 0378-3758
- eISSN
- 1873-1171
- Number of pages
- 10
- Language
- English
- Date published
- 02/01/2009
- Academic Unit
- Biostatistics; Statistics and Actuarial Science; Public Policy Center (Archive)
- Record Identifier
- 9984283854502771
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