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Spurious interaction as a result of categorization.

Magne Thoresen1

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Converting continuous variables to categories in regression models can create artificial interaction effects. This practice should be avoided in epidemiological and clinical research to ensure accurate results.

Keywords:
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Area of Science:

  • Epidemiology
  • Biostatistics
  • Clinical Research

Background:

  • Continuous variables are frequently converted into categorical variables in applied research.
  • These categorized variables are then utilized as exposure variables in regression models.
  • This common practice faces statistical objections, with this paper presenting an additional argument against it.

Purpose of the Study:

  • To demonstrate that categorizing continuous variables can induce spurious interactions in regression models.
  • To provide analytical expressions for spurious interaction occurrence in linear models with normally distributed variables.
  • To interpret these findings through the lens of measurement error.

Main Methods:

  • Analytical derivations for linear regression models with normally distributed exposure variables.
  • Simulation studies to validate analytical results across different variable distributions.
  • Examination of interaction effects when two variables are categorized at the same cut point.

Main Results:

  • Categorization can lead to spurious interactions in multiple regression models.
  • In linear models with two normally distributed exposure variables, spurious interaction arises unless categorized at the median or variables are uncorrelated.
  • Simulation results confirm the general effect of categorization, with cut point choice impacting results across distributions.

Conclusions:

  • Categorizing continuous exposure variables introduces significant problems, including spurious interaction effects.
  • This practice should be discontinued in research.
  • Alternative statistical methods should be explored for analyzing continuous exposure variables.