CoChat
Sign Up
Cited by 273Meta-analysis and systematic reviewsStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Assessing baseline imbalance in randomised trials: implications for the Cochrane risk of bias tool: Baseline imbalance assessment and risk of bias

Corbett, Mark S., Higgins, Julian P. T., Woolacott, Nerys F.·2014·Research Synthesis Methods
DOI 10.1002/jrsm.1090
Open this paper in CoChat — free account

Abstract

A key component of the Cochrane Collaboration's risk of bias tool for critically evaluating randomised trials is the consideration of whether baseline characteristics of the treatment groups being compared are systematically different. Considered under the domain of 'selection bias', this is currently evaluated by looking at the methods of randomisation and specifically at the generation of the randomised allocation sequence and the concealment of this sequence during the process of randomisation. Assessment of the actual similarity of baseline variables across groups in demographic and clinical characteristics is seldom performed. Even when performed, the link with selection bias is sometimes not considered. Methods of randomisation and allocation concealment are often poorly reported in published trials, yet baseline data tables are presented in a large majority of trial reports. In this article, we propose that assessment of trial baseline data should form a key and prominent part of selection bias judgements when using the risk of bias tool. We outline the possible benefits from using this approach, including reduced uncertainty in systematic review conclusions, reduced risk of chance findings being ascribed to treatment effects and better use of available evidence by a more considered approach to evaluating studies using imperfect randomisation and allocation methods.

Integrity signals

Registry

Crossref · 10.1002/jrsm.1090

Cites

14 of 14 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?

  • Adding baseline assessments can change the judgement about risk of selection bias compared with using methods reporting alone.

    “judgements highlighted in bold indicate situations in which the addition of baseline assessments would change the judgement about risk of selection bias compared with using methods reporting alone.”

    From Mindfulness-based interventions for adults with type 2 diabetes mellitus (2021) · cites this paper as Corbett 2014

  • Selection bias decisions for studies that reported unadjusted analyses can be made by comparing results obtained using method details alone with results obtained using method details and study baseline information.

    “Selection bias decisions for studies that reported unadjusted analyses: comparison of results obtained using method details alone versus results obtained using method details and study baseline information”

    From Mindfulness-based interventions for adults with type 2 diabetes mellitus (2021) · cites this paper as Corbett 2014

Cites

Flagged first, then by citations

2 of the 16 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Corbett, M. S., Higgins, J. P. T., & Woolacott, N. F.
Affiliations
University of York (GB)
Corresponding author
Corbett, Mark S.
ORCID
Venue
Research Synthesis Methods
Publisher
Wiley
Published
2013-08-01
Volume
5
Issue
1
Pages
79-85
ISSN
1759-2879 (ISSN-L)
DOI
10.1002/jrsm.1090
OpenAlex
W1858763784
Cited by
273
References
16
Citations by year
2026: 20 · 2025: 42 · 2024: 34 · 2023: 16 · 2022: 22 · 2021: 23 · 2020: 29 · 2019: 22 · 2018: 18 · 2017: 16 · 2016: 23 · 2015: 7 · 2013: 1
FWCI
11.40
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Research Synthesis Methods (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
baseline imbalance, randomised trials, risk of bias, Cochrane risk of bias tool, selection bias, allocation concealment, randomization methods, baseline characteristics, trial reporting quality
MeSH terms
Algorithms, Data Interpretation, Statistical, Sensitivity and Specificity, Software, Reproducibility of Results, Selection Bias, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Patient Selection

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