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Dichotomizing continuous biomedical data using flexible thresholds, termed "promiscuous dichotomisation," can create the illusion of a true effect. This practice significantly increases the risk of false positive results in scientific research.

Keywords:
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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Data Analysis

Background:

  • Continuous biomedical data is frequently dichotomized for analysis, a practice discouraged by statisticians.
  • Dichotomization reduces statistical power and can obscure important trends.
  • This paper highlights dichotomization as a tool for data manipulation via 'promiscuous dichotomisation,' offering researchers undue flexibility.

Purpose of the Study:

  • To quantify the probability of generating a false positive effect by manipulating dichotomous thresholds.
  • To assess the potential for engineered spurious findings in uniformly distributed data.
  • To illustrate the manipulation of thresholds using real-world health data (NHANES).

Main Methods:

  • Analytical approaches and Monte-Carlo simulations were used to estimate spurious findings.
  • Quantified the expected number of false positives from threshold manipulation.
  • Demonstrated engineered spurious relationships in NHANES data, e.g., blood glucose and herpes status.

Main Results:

  • Even small sample sizes ([Formula: see text]) can yield false positive rates of [Formula: see text].
  • Larger samples show elevated false positive rates ([Formula: see text] and [Formula: see text]) even with low-count exclusion.
  • Threshold manipulation is a viable method for creating false positive results across most configurations.

Conclusions:

  • Manipulating cut-off points is a significant source of data manipulation in published science.
  • The increasing availability of large health databases exacerbates this issue.
  • Discusses implications and methods for identifying potential promiscuous dichotomisation.