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Logistic Regression and Related Methods

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Principles and Practice of Clinical Trials

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Abstract

Inference on binary outcomes is a common goal in clinical trials and case-control studies. Logistic regression is the usual approach to estimate treatment effect adjusted for categorical and continuous confounding variables. In this chapter, model building, interpretation of parameters, diagnostics, and inference to small sample sizes are discussed. At last, a case study is presented applying the proposed analytic strategies.

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Author information

Authors and Affiliations

  1. Biostatistics and Bioinfomatics Research Center, Samuel Oschin Cancer Center, Cedars Sinai Medical Center, Los Angeles, California, USA

    Márcio A. Diniz

  2. Department of Statistics, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil

    Tiago M. Magalhães

Authors
  1. Márcio A. Diniz
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  2. Tiago M. Magalhães
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Corresponding author

Correspondence to Márcio A. Diniz .

Editor information

Editors and Affiliations

  1. Samuel Oschin Comprehensive Cancer Insti, WEST HOLLYWOOD, CA, USA

    Steven Piantadosi

  2. Bloomberg School of Public Health, Johns Hopkins Center for Clinical Trials Bloomberg School of Public Health, Baltimore, MD, USA

    Curtis L. Meinert

Section Editor information

  1. Dept. of Biostatistics and Bioinformatics,Basic Science Divison, Duke University, School of Medicine, Durham, North Carolina, USA

    Stephen George

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Diniz, M.A., Magalhães, T.M. (2020). Logistic Regression and Related Methods. In: Piantadosi, S., Meinert, C. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52677-5_122-2

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  • DOI: https://doi.org/10.1007/978-3-319-52677-5_122-2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52677-5

  • Online ISBN: 978-3-319-52677-5

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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Chapter history

  1. Latest

    Logistic Regression and Related Methods
    Published:
    26 February 2020

    DOI: https://doi.org/10.1007/978-3-319-52677-5_122-2

  2. Original

    Logistic Regression and Related Methods
    Published:
    06 February 2020

    DOI: https://doi.org/10.1007/978-3-319-52677-5_122-1

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