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Statistics for the Clinician I: Categorical Variables.

Roy Madrid1, Jordan A Buttner1, Mark Shilling2

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This article explains common statistical analyses for categorical variables in clinical research. It covers chi-square tests, Fisher's exact tests, relative risk, and odds ratios for better data interpretation.

Keywords:
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Area of Science:

  • Clinical Research Statistics
  • Biostatistics
  • Data Analysis in Medicine

Background:

  • Categorical variables are fundamental in clinical research.
  • Clinicians require enhanced statistical knowledge to interpret medical literature and improve research.
  • Understanding common statistical tests and measures of association is crucial.

Purpose of the Study:

  • To introduce clinicians to the uses and analyses of categorical variables.
  • To provide a guide for understanding statistical tests and effect sizes in clinical research.
  • To enhance critical engagement with medical literature and research design.

Main Methods:

  • Description and demonstration of chi-square and Fisher's exact tests.
  • Explanation of the logic, interpretation, and limitations of these association tests.
  • Introduction to relative risk (RR) and odds ratio (OR) as measures of effect size.

Main Results:

  • Chi-square and Fisher's exact tests are key for analyzing categorical data associations.
  • Relative risk and odds ratios quantify the effect size in categorical outcome analyses.
  • Understanding these methods aids in accurate interpretation of clinical study results.

Conclusions:

  • This article equips clinicians with essential statistical tools for categorical data.
  • Improved understanding of statistical tests and effect sizes enhances research quality.
  • Clinicians can better interpret literature and refine their own research endeavors.