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Modeling Longitudinal Data, I: Principles of Multivariate Analysis

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Clinical Epidemiology

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 473))

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Abstract

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors' impact on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precision around the point estimates (confidence intervals).

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Correspondence to Pietro Ravani .

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© 2008 Humana Press, a part of Springer Science+Business Media, LLC

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Ravani, P., Barrett, B., Parfrey, P. (2008). Modeling Longitudinal Data, I: Principles of Multivariate Analysis. 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_3

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  • DOI: https://doi.org/10.1007/978-1-59745-385-1_3

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-964-2

  • Online ISBN: 978-1-59745-385-1

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