CoChat
Sign Up
Cited by 580Statistical Methods in Clinical TrialsMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives

Open access
Brookes, S. T., Whitley, E., Peters, T. J., Mulheran, P. A., Egger, M., Davey Smith, G.·2001·Health Technology Assessment
DOI 10.3310/hta5330
Open this paper in CoChat — free account

Abstract

Background Subgroup analyses are common in randomised controlled trials (RCTs). There are many easily accessible guidelines on the selection and analysis of subgroups but the key messages do not seem to be universally accepted and inappropriate analyses continue to appear in the literature. This has potentially serious implications because erroneous identification of differential subgroup effects may lead to inappropriate provision or withholding of treatment. Objectives (1) To quantify the extent to which subgroup analyses may be misleading. (2) To compare the relative merits and weaknesses of the two most common approaches to subgroup analysis: separate (subgroup-specific) analyses of treatment effect and formal statistical tests of interaction. (3) To establish what factors affect the performance of the two approaches. (4) To provide estimates of the increase in sample size required to detect differential subgroup effects. (5) To provide recommendations on the analysis and interpretation of subgroup analyses. Methods The performances of subgroup-specific and formal interaction tests were assessed by simulating data with no differential subgroup effects and determining the extent to which the two approaches (incorrectly) identified such an effect, and simulating data with a differential subgroup effect and determining the extent to which the two approaches were able to (correctly) identify it. Initially, data were simulated to represent the 'simplest case' of two equal-size…

Integrity signals

Registry

Crossref · 10.3310/hta5330

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Interaction effects require larger sample sizes for adequate power, and power depends on allele frequencies and exposure to early life stress.

    “Achieving sufficient statistical power presents a special challenge because interaction effects require larger numbers of subjects for adequate power (Brookes et al. 2001; Luan et al. 2001; Uher 2008), and power depends on allele frequencies in addition to exposure to ELS (Munafo et al. 2009).”

    From Gene–environment interactions: early life stress and risk for depressive and anxiety disorders (2011) · cites this paper as Brookes et al. 2001

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Brookes, S. T., Whitley, E., Peters, T. J., Mulheran, P. A., Egger, M., & Davey Smith, G.
Affiliations
  • University of Bristol (GB)
  • University of Reading (GB)
  • Department of Social Medicine, University of Bristol, UK
ORCID
Venue
Health Technology Assessment
Publisher
National Institute for Health and Care Research
Published
2001
Volume
5
Issue
33
Pages
1-56
ISSN
  • 1366-5278 (ISSN-L)
  • 2046-4924
DOI
10.3310/hta5330
OpenAlex
W2165414412
Cited by
580
References
3
Citations by year
2026: 19 · 2025: 16 · 2024: 16 · 2023: 28 · 2022: 17 · 2021: 30 · 2020: 24 · 2019: 18 · 2018: 21 · 2017: 32 · 2016: 29 · 2015: 26 · 2014: 27 · 2013: 27 · 2012: 24
FWCI
11.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Health Technology Assessment (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
Funders
Health Technology Assessment programme (97/40/03)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
subgroup analysis, interaction testing, false positives, false-negative results, statistical power, sample size, simulation study, treatment effect heterogeneity, binary outcomes, continuous outcomes, survival outcomes, randomized controlled trials
MeSH terms
Data Interpretation, Statistical, Humans, Research Design, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Randomized Controlled Trials as Topic, Sample Size

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review