Subgroup analyses in randomised controlled trials frequently categorise continuous subgroup information

S. Faye Williamson, Michael J Grayling, Adrian P Mander, Nurulamin Noor, Joshua Savage, Christina Yap, James M S Wason

Research output: Working paper/PreprintPreprint


Objective: To investigate how subgroup analyses of published Randomised Controlled Trials (RCTs) are performed when subgroups are created from continuous variables.

Study design and setting: We carried out a review of RCTs published in 2016–2021 that included subgroup analyses. Information was extracted on whether any of the subgroups were based on continuous variables and, if so, how they were analysed.

Results: Out of 428 reviewed papers, 258 (60.4%) reported RCTs with a subgroup analysis. Of these, 178/258 (69%) had at least one subgroup formed from a continuous variable and 14/258 (5.4%) were unclear. The vast majority (169/178, 94.9%) dichotomised the continuous variable and treated the subgroup as categorical. The most common way of dichotomising was using a pre-specified cutpoint (129/169, 76.3%), followed by a data-driven cutpoint (26/169, 15.4%), such as the median.

Conclusion: It is common for subgroup analyses to use continuous variables to define subgroups. The vast majority dichotomise the continuous variable and, consequently, may lose substantial amounts of statistical information (equivalent to reducing the sample size by at least a third). More advanced methods that can improve efficiency, through optimally choosing cutpoints or directly using the continuous information, are rarely used.
Original languageEnglish
Publication statusPublished - 7 Apr 2022

Publication series

NameSSRN Electronic Journal
ISSN (Print)1556-5068


  • Continuous variables
  • Dichotomization
  • Moderator analysis
  • randomized controlled trials
  • Subgroup analysis


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