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[Introduction to regression models].

C Hill1

  • 1Institut Gustave-Roussy, 94805 Villejuif. hill@igr.fr

Bulletin Du Cancer
|September 2, 2000
PubMed
Summary
This summary is machine-generated.

This study clarifies common regression models for clinicians. Understanding these statistical tools, like logistic regression and Cox models, aids critical interpretation and collaboration with statisticians.

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Research Methodology

Context:

  • Regression models are frequently utilized in clinical research.
  • Clinicians often perceive complex statistical models as essential for publication in high-impact journals.
  • There is a tendency for non-statisticians to interpret regression model results without critical evaluation.

Purpose:

  • To demystify common regression models within a unified framework.
  • To provide clear guidance on interpreting the results of multivariate, logistic regression, and Cox models.
  • To empower investigators to engage in more informed discussions with statisticians regarding these analytical methods.

Summary:

  • This work presents a unified approach to understanding prevalent regression models, including multivariate, logistic regression, and Cox models.

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  • The explanation focuses on practical interpretation of model outputs, moving beyond their perceived complexity.
  • The goal is to bridge the gap between clinical researchers and statistical methodologies.

    • Impact:

    • Enhance the critical appraisal of regression model results by clinicians.
    • Facilitate better communication and collaboration between clinical investigators and statisticians.
    • Improve the rigor and understanding of statistical analyses in medical publications.