Abstract
In longitudinal studies, the relationship between exposure and disease can be measured once or multiple times while participants are monitored over time. Traditional regression techniques are used to model outcome data when each epidemiological unit is observed once. These models include generalized linear models for quantitative continuous, discrete, or qualitative outcome responses and models for time-to-event data. When data come from the same subjects or group of subjects, observations are not independent and the underlying correlation needs to be addressed in the analysis. Under these circumstances, extended models are necessary to handle complexities related to clustered data and repeated measurements of time-varying predictors or outcomes.
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Ravani P, Parfrey P, Murphy S, Gadag V, Barrett B (2008) Clinical research of kidney diseases IV: Standard regression models. Nephrol Dial Transplant 23, 475–82
Ravani P, Parfrey P, Gadag V, Malberti F, Barrett B (2008) Clinical research of kidney diseases V: extended analytic models. Nephrol Dial Transplant 23, 1484–92
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Ravani, P., Barrett, B., Parfrey, P. (2008). Modeling Longitudinal Data, II: Standard Regression Models and Extensions. In: Barrett, B., Parfrey, P. (eds) Clinical Epidemiology. Methods in Molecular Biology™, vol 473. Humana Press. https://doi.org/10.1007/978-1-59745-385-1_4
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DOI: https://doi.org/10.1007/978-1-59745-385-1_4
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