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Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions

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Clinical Epidemiology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1281))

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. In these circumstances extended models are necessary to handle complexities related to clustered data, and repeated measurements of time-varying predictors and/or outcomes.

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Author information

Authors and Affiliations

  1. Division of Nephrology, Department of Medicine, University of Calgary, 1403, 29th St NW (Foothills Medical Centre), Calgary, AB, Canada, T2N 2T9

    Pietro Ravani

  2. Clinical Epidemiology Unit, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL, Canada

    Brendan J. Barrett & Patrick S. Parfrey

Authors
  1. Pietro Ravani
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  2. Brendan J. Barrett
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  3. Patrick S. Parfrey
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Corresponding author

Correspondence to Pietro Ravani .

Editor information

Editors and Affiliations

  1. Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada

    Patrick S. Parfrey

  2. Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada

    Brendan J. Barrett

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Ravani, P., Barrett, B.J., Parfrey, P.S. (2015). Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions. In: Parfrey, P., Barrett, B. (eds) Clinical Epidemiology. Methods in Molecular Biology, vol 1281. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2428-8_6

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  • DOI: https://doi.org/10.1007/978-1-4939-2428-8_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2427-1

  • Online ISBN: 978-1-4939-2428-8

  • eBook Packages: Springer Protocols

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