Abstract
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for most methods described in previous chapters. In some experiments, the amount of random variation differs between experimental groups. In other experiments, there are multiple sources of variability, such as both between-subject variation and technical variation. As demonstrated in this chapter, linear mixed effects models provide a versatile and powerful framework in which to address research objectives efficiently and appropriately.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Zeger, S. L., Liang, K. Y., and Albert, P. S. (1988) Models for longitudinal data: a generalized estimating equation approach. Biometrics 44, 1049–1060.
McLean, R. A., Sanders, W. L., and Stroup, W. W. (1991) A unified approach to mixed linear models. Am. Stat. 45, 54–64.
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. (2002) SAS ® System for Mixed Models. Cary, SAS Institute Inc.
Redfield, M. M., Jacobsen, S. J., Burnett, J. C. Jr., Mahoney, D. W., Bailey, K. R., and Rodeheffer, R. J. (2003) Burden of systolic and diastolic ventricular dysfunction in the community: appreciating the scope of the heart failure epidemic. JAMA 289(2), 194–202.
Melton, L. J. III, Atkinson, E. J., O’Connor, M. K., O’Fallon, W. M., and Riggs, B. L. (1998) Bone density and fracture risk in men. J. Bone Miner. Res. 13, 1915–1923.
Diggle, P. J., Liang, K., and Zeger, S. L. (1995) Analysis of Longitudinal Data. New York, Oxford University Press.
Rao, C. R. (1965) The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves. Biometrika 52, 447–468.
Verbeke, G., and Molenberghs, G. (2000) Linear Mixed Models for Longitudinal Data. New York, Springer-Verlag.
Davidian, M., and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measures Data. New York, Chapman & Hall.
Stroup, W. W. (1999) Mixed model procedures to assess power, precision, and sample size in the design of experiments. ASA Proceedings of the Biopharmaceutical Section. Alexandria, American Statistical Association, pp. 15–24.
McCulloch, C. E. (2003) Generalized linear mixed models. NSF-CBMS Regional Conference Series in Probability and Statistics 7. San Francisco, Institute of Mathematical Statistics.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Humana Press Inc., Totowa, NJ
About this protocol
Cite this protocol
Oberg, A.L., Mahoney, D.W. (2007). Linear Mixed Effects Models. In: Ambrosius, W.T. (eds) Topics in Biostatistics. Methods in Molecular Biology™, vol 404. Humana Press. https://doi.org/10.1007/978-1-59745-530-5_11
Download citation
DOI: https://doi.org/10.1007/978-1-59745-530-5_11
Publisher Name: Humana Press
Print ISBN: 978-1-58829-531-6
Online ISBN: 978-1-59745-530-5
eBook Packages: Springer Protocols