Journal article
Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms
Journal of the Royal Statistical Society. Series B, Statistical methodology, Vol.67(1), pp.167-182
Received March 2003. Final revision July 2004
02/2005
DOI: 10.1111/j.1467-9868.2005.00494.x
Abstract
In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.
Details
- Title: Subtitle
- Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms
- Creators
- Chandan Saha - Indiana UniversityMichael P. Jones - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Royal Statistical Society. Series B, Statistical methodology, Vol.67(1), pp.167-182
- Edition
- Received March 2003. Final revision July 2004
- Publisher
- Blackwell Publishing
- DOI
- 10.1111/j.1467-9868.2005.00494.x
- ISSN
- 1369-7412
- eISSN
- 1467-9868
- Number of pages
- 16
- Language
- English
- Date published
- 02/2005
- Academic Unit
- Biostatistics; Statistics and Actuarial Science; Public Policy Center (Archive)
- Record Identifier
- 9984283714802771
Metrics
16 Record Views