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Subgroup analyses in randomized controlled trials frequently categorized continuous subgroup information.

S Faye Williamson1, Michael J Grayling1, Adrian P Mander2

  • 1Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.

Journal of Clinical Epidemiology
|July 5, 2022
PubMed
Summary

Researchers found that most randomized controlled trials (RCTs) dichotomize continuous variables for subgroup analysis, losing significant statistical power. Advanced methods for using continuous data are rarely employed.

Keywords:
CategorizationContinuous variablesDichotomizationModerator analysisRandomized controlled trialsSubgroup analysis

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • Subgroup analyses are frequently employed in Randomized Controlled Trials (RCTs).
  • Investigating the methods used when continuous variables define these subgroups is crucial for accurate interpretation.
  • Understanding current practices ensures methodological rigor in clinical research.

Approach:

  • A systematic review was conducted on RCTs published between 2016 and 2021.
  • Data extraction focused on the presence and analysis of subgroups derived from continuous variables.
  • 428 papers were reviewed, with 258 reporting subgroup analyses.

Key Points:

  • 69% of RCTs with subgroup analyses utilized continuous variables.
  • The vast majority (94.9%) dichotomized continuous variables, treating them as categorical.
  • Pre-specified cutpoints were the most common dichotomization method (76.3%).

Conclusions:

  • Dichotomizing continuous variables in subgroup analyses is common but leads to substantial loss of statistical information.
  • Advanced analytical methods that preserve continuous data or optimize cutpoints are underutilized.
  • Improved methodologies are needed to enhance the efficiency and validity of subgroup analyses in clinical trials.