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An overview of methods for the analysis of longitudinal data
Abstract
This paper reviews statistical methods for the analysis of discrete and continuous longitudinal data. The relative merits of longitudinal and cross‐sectional studies are discussed. Three approaches, marginal, transition and random effects models, are presented with emphasis on the distinct interpretations of their coefficients in the discrete data case. We review generalized estimating equations for inferences about marginal models. The ideas are illustrated with analyses of a 2 × 2 crossover trial with binary responses and a randomized longitudinal study with a count outcome.
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Cited by
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Twisk, J., & de Vente, W. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00476-0 · Cited by 527
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Costello, E. J., Erkanli, A., Fairbank, J. A., & Angold, A. (2002) · Journal of Traumatic Stress · doi:10.1023/a:1014851823163 · Cited by 511
Handling drop‐out in longitudinal studies
Hogan, J. W., Roy, J., & Korkontzelou, C. (2004) · Statistics in Medicine · doi:10.1002/sim.1728 · Cited by 404
Wang, R., Aung, M. S. H., Abdullah, S., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J., Merrill, M., Scherer, E. A., et al. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971740 · Cited by 257
Predicting Improvement in Gait After Stroke: A Longitudinal Prospective Study
Kollen, B., van de Port, I., Lindeman, E., Twisk, J., & Kwakkel, G. (2005) · Stroke · doi:10.1161/01.str.0000190839.29234.50 · Cited by 269
Metadata
- Authors
- Zeger, S. L., & Liang, K.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1992-01
- Volume
- 11
- Issue
- 14-15
- Pages
- 1825-1839
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2171042621
- Cited by
- 678
- References
- 0
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- closed
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