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Maximum Accuracy Machine Learning Statistical Analysis—A Novel Approach

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Cancer Drug Safety and Public Health Policy

Part of the book series: Cancer Treatment and Research ((CTAR,volume 184))

Abstract

Logistic regression is a statistical tool of paramount significance in the field of epidemiology1 and ranks as one of the most frequently published multivariable analyses for designs involving a single binary dependent variable and one or more independent variables in the fields of public health2,3 and medical4 research.

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Authors and Affiliations

  1. SONAR (Southern Network on Adverse Reactions) Program, University of South Carolina College of Pharmacy, Columbia, SC, 29208, USA

    Shannon Ugarte, Paul Yarnold, Paul Ray, Kevin Knopf, Shamia Hoque, Matthew Taylor & Charles L. Bennett

Authors
  1. Shannon Ugarte
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  2. Paul Yarnold
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  3. Paul Ray
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  4. Kevin Knopf
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  5. Shamia Hoque
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  6. Matthew Taylor
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  7. Charles L. Bennett
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Corresponding author

Correspondence to Charles L. Bennett .

Editor information

Editors and Affiliations

  1. College of Pharmacy, University of South Carolina, Columbia, SC, USA

    Charles Bennett

  2. College of Pharmacy, University of South Carolina, Columbia, SC, USA

    Courtney Lubaczewski

  3. College of Pharmacy, University of South Carolina, Columbia, SC, USA

    Bartlett Witherspoon

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Ugarte, S. et al. (2022). Maximum Accuracy Machine Learning Statistical Analysis—A Novel Approach. In: Bennett, C., Lubaczewski, C., Witherspoon, B. (eds) Cancer Drug Safety and Public Health Policy. Cancer Treatment and Research, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-04402-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-04402-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04401-4

  • Online ISBN: 978-3-031-04402-1

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