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Logistic Regression

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Topics in Biostatistics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 404))

Abstract

The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as “statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable.” Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin’s lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.

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Author information

Authors and Affiliations

  1. Cincinnati Children’s Hospital Medical Center, Cincinnati, OH

    Todd G. Nick PhD

  2. Cincinnati Children’s Hospital, Cincinnati, OH

    Kathleen M. Campbell MD

Authors
  1. Todd G. Nick PhD
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  2. Kathleen M. Campbell MD
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Editor information

Editors and Affiliations

  1. Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC

    Walter T. Ambrosius

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© 2007 Humana Press Inc., Totowa, NJ

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Nick, T.G., Campbell, K.M. (2007). Logistic Regression. 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_14

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  • DOI: https://doi.org/10.1007/978-1-59745-530-5_14

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-531-6

  • Online ISBN: 978-1-59745-530-5

  • eBook Packages: Springer Protocols

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