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Cut points and contexts.

Evan L Busch1,2

  • 1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.

Cancer
|August 23, 2021
PubMed
Summary
This summary is machine-generated.

Categorizing continuous variables into discrete groups is common in research and practice. However, the choice of cut points is subjective and creates trade-offs, meaning no single categorization is objectively best.

Keywords:
data analysisstatistical data interpretationstatisticstranslational medical researchtranslational medical science

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Area of Science:

  • Statistics
  • Data Analysis
  • Research Methodology

Background:

  • Continuous variables are frequently categorized in research, policy, and practice.
  • Traditional statistical advice cautions against categorization due to information loss and precision reduction.

Purpose of the Study:

  • To examine the assumption of an objectively "best" set of cut points for categorizing continuous variables.
  • To explore how different cut point choices introduce substantive trade-offs in statistical relationships.

Main Methods:

  • Conceptual analysis of variable categorization.
  • Exploration of statistical relationships between categorized and other variables.
  • Discussion of limitations in cut point selection approaches.

Main Results:

  • The selection of cut points for a continuous variable is unlikely to yield a single, objectively correct categorization.
  • Different cut point choices influence statistical relationships between variables, creating context-dependent trade-offs.
  • The existence of these trade-offs complicates the determination of an optimal categorization.

Conclusions:

  • No single categorization of a continuous variable can be deemed objectively best due to inherent trade-offs.
  • Decisions involving categorized variables in research, policy, or practice require flexibility and humility.
  • Contextual trade-offs often argue against categorization or necessitate careful consideration of its implications.