CoChat
Sign Up
Cited by 1,167Statistical Methods in Clinical TrialsMeta-analysis and systematic reviewsStatistical Methods in Epidemiology

Subgroup analysis and other (mis)uses of baseline data in clinical trials

Assmann, Susan F, Pocock, Stuart J, Enos, Laura E, Kasten, Linda E·2000·The Lancet
DOI 10.1016/s0140-6736(00)02039-0
Open this paper in CoChat — free account

Abstract

Background Baseline data collected on each patient at randomisation in controlled clinical trials can be used to describe the population of patients, to assess comparability of treatment groups, to achieve balanced randomisation, to adjust treatment comparisons for prognostic factors, and to undertake subgroup analyses. We assessed the extent and quality of such practices in major clinical trial reports.Methods A sample of 50 consecutive clinical-trial reports was obtained from four major medical journals during July to September, 1997. We tabulated the detailed information on uses of baseline data by use of a standard form.Findings Most trials presented baseline comparability in a table. These tables were often unduly large, and about half the trials inappropriately used significance tests for baseline comparison. Methods of randomisation, including possible stratification, were often poorly described. There was little consistency over whether to use covariate adjustment and the criteria for selecting baseline factors for which to adjust were often unclear. Most trials emphasised the simple unadjusted results and covariate adjustment usually made negligible difference. Two-thirds of the reports presented subgroup findings, but mostly without appropriate statistical tests for interaction. Many reports put too much emphasis on subgroup analyses that commonly lacked statistical power.Interpretation Clinical trials need a predefined statistical analysis plan for uses of baseline data, especially covariate-adjusted analyses and subgroup analyses. Investigators and journals need to adopt improved standards of statistical reporting, and exercise caution when drawing conclusions from subgroup findings.

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(00)02039-0

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

This paper’s full text isn’t openly available, 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?

  • Each model considered baseline factors differing between treatment arms and associated with the outcome, preserving statistical power and ensuring precise 95% confidence intervals.

    “Additionally, each model considered baseline factors differing between treatment arms and associated with the outcome [73], preserving statistical power and ensuring precise 95% confidence intervals [74].”

    From Can an Acceptance and Commitment Therapy-Based Smartphone App Help Individuals with Mental Health Disorders Quit Smoking? (2024) · cites this paper as [73]

  • In a review of 50 RCTs in high-impact journals, 70% of trials tested interactions.

    “In 2000, Assmann et al 3 reviewed 50 RCTs in high-impact journals and found that 70% of trials tested interactions, but only 43% of the studies testing interaction reported the test they used, and 37% of them reported P-values only.”

    From Understanding of interaction (subgroup) analysis in clinical trials (2019) · cites this paper as [3]

  • Among studies testing interaction, only 43% reported the test they used.

    “In 2000, Assmann et al 3 reviewed 50 RCTs in high-impact journals and found that 70% of trials tested interactions, but only 43% of the studies testing interaction reported the test they used, and 37% of them reported P-values only.”

    From Understanding of interaction (subgroup) analysis in clinical trials (2019) · cites this paper as [3]

  • 37% of studies testing interaction reported P-values only.

    “In 2000, Assmann et al 3 reviewed 50 RCTs in high-impact journals and found that 70% of trials tested interactions, but only 43% of the studies testing interaction reported the test they used, and 37% of them reported P-values only.”

    From Understanding of interaction (subgroup) analysis in clinical trials (2019) · cites this paper as [3]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Assmann, S. F., Pocock, S. J., Enos, L. E., & Kasten, L. E.
Affiliations
  • New England Research Institutes (US)
  • London School of Hygiene & Tropical Medicine (GB)
Corresponding author
Pocock, Stuart J
ORCID
Pocock, Stuart J
Venue
The Lancet
Publisher
Elsevier BV
Published
2000-03
Volume
355
Issue
9209
Pages
1064-1069
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
DOI
10.1016/s0140-6736(00)02039-0
OpenAlex
W2108233388
Cited by
1,167
References
32
Citations by year
2026: 25 · 2025: 48 · 2024: 35 · 2023: 40 · 2022: 34 · 2021: 40 · 2020: 45 · 2019: 48 · 2018: 38 · 2017: 48 · 2016: 60 · 2015: 51 · 2014: 48 · 2013: 58 · 2012: 53
FWCI
41.59
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • The Lancet (publishedVersion)
  • PubMed (publishedVersion)
  • DigitalGeorgetown (Georgetown University Library) (submittedVersion)
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 › Statistical Methods in Epidemiology
Keywords
baseline data, subgroup analysis, covariate adjustment, clinical trial reporting, statistical reporting standards, randomization, stratification, interaction testing, statistical power, prognostic factors, analysis plan
MeSH terms
Data Collection, Data Interpretation, Statistical, Humans, Bias, Randomized Controlled Trials as Topic

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