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Logistic Regression

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Regression Methods in Biostatistics

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Patients testing positive for a sexually transmitted disease at a clinic are compared to patients with negative tests to investigate the effectiveness of a new barrier contraceptive. One-month mortality following coronary artery bypass graft surgery is compared in groups of patients receiving different dosages of beta blockers. Many clinical and epidemiological studies generate outcomes which take on one of two possible values, reflecting presence/absence of a condition or characteristic at a particular time, or indicating whether a response occurred within a defined period of observation. In addition to evaluating a predictor of primary interest, it is important to investigate the importance of additional variables that may influence the observed association and therefore alter our inferences about the nature of the relationship. In evaluating the effect of contraceptive use in the first example, it would be clearly important to control for age in addition to behaviors potentially linked to infection risk. In the second example, a number of demographic and clinical variables may be related to both the mortality outcome and treatment regime. Both of these examples are characterized by binary outcomes and multiple predictors, some of which are continuous.

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, University of California, San Francisco, Parnassas Ave. 500 MU-420 West, 94143, San Francisco, California, USA

    Eric Vittinghoff, David V. Glidden & Stephen C. Shiboski

  2. Department of Epidemiology and Biostatistics, University of California, San Francisco, Berry 185 Suite 5700, 94107, San Francisco, California, USA

    Prof. Charles E. McCulloch

Authors
  1. Eric Vittinghoff
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  2. David V. Glidden
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  3. Stephen C. Shiboski
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  4. Prof. Charles E. McCulloch
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Vittinghoff, E., Glidden, D.V., Shiboski, S.C., McCulloch, C.E. (2012). Logistic Regression. In: Regression Methods in Biostatistics. Statistics for Biology and Health. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1353-0_5

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