The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
When are randomised trials unnecessary? Picking signal from noise
Open accessAbstract
Although randomised trials are widely accepted as the ideal way of obtaining unbiased estimates of treatment effects, some treatments have dramatic effects that are highly unlikely to reflect inadequately controlled biases. We compiled a list of historical examples of such effects and identified the features of convincing inferences about treatment effects from sources other than randomised trials. A unifying principle is the size of the treatment effect (signal) relative to the expected prognosis (noise) of the condition. A treatment effect is inferred most confidently when the signal to noise ratio is large and its timing is rapid compared with the natural course of the condition. For the examples we considered in detail the rate ratio often exceeds 10 and thus is highly unlikely to reflect bias or factors other than a treatment effect. This model may help to reduce controversy about evidence for treatments whose effects are so dramatic that randomised trials are unnecessary.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Grading quality of evidence and strength of recommendations
(2004) · BMJ · doi:10.1136/bmj.328.7454.1490 · Cited by 8,477
Evaluating non-randomised intervention studies
Deeks, J., Dinnes, J., D'Amico, R., Sowden, A., Sakarovitch, C., Song, F., Petticrew, M., & Altman, D. (2003) · Health Technology Assessment · doi:10.3310/hta7270 · Cited by 2,907
When are observational studies as credible as randomised trials?
Vandenbroucke, J. P. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16261-2 · Cited by 538 · cited 1×
“and non-randomised cohort studies. Further research is required to obtain better estimates of the plausible limits of bias in different types of non-randomised study designs.”
Background
Cited by
Developing and evaluating complex interventions: the new Medical Research Council guidance
Craig, P., Dieppe, P., Macintyre, S., Michie, S., Nazareth, I., & Petticrew, M. (2008) · BMJ · doi:10.1136/bmj.a1655 · Cited by 10,995
“It is important designs preferable (Black, 1996; Glasziou et al., 2007), but”
GRADE guidelines: 3. Rating the quality of evidence
Balshem, H., Helfand, M., Schünemann, H. J., Oxman, A. D., Kunz, R., Brozek, J., Vist, G. E., Falck-Ytter, Y., Meerpohl, J., & Norris, S. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.07.015 · Cited by 8,285
What is ‘quality of evidence’ and why is it important to clinicians?
Guyatt, G. H., Oxman, A. D., Kunz, R., Vist, G. E., Falck-Ytter, Y., & Schünemann, H. J. (2008) · BMJ · doi:10.1136/bmj.39490.551019.be · Cited by 3,250
Diagnosis and treatment of hyperprolactinemia: an Endocrine Society Clinical Practice Guideline
Melmed, S., Casanueva, F. F., Hoffman, A. R., Kleinberg, D. L., Montori, V. M., Schlechte, J. A., & Wass, J. A. H. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2010-1692 · Cited by 1,844
Interpretation of the evidence for the efficacy and safety of statin therapy
Collins, R., Reith, C., Emberson, J., Armitage, J., Baigent, C., Blackwell, L., Blumenthal, R., Danesh, J., Smith, G. D., DeMets, D., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31357-5 · Cited by 1,857
Metadata
- Authors
- Glasziou, P., Chalmers, I., Rawlins, M., & McCulloch, P.
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2007-02-15
- Volume
- 334
- Issue
- 7589
- Pages
- 349-351
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- OpenAlex
- W1985075719
- Cited by
- 589
- References
- 3
- FWCI
- 35.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.bmj.org/licenses/tdm/1.0/terms-and-conditions.html (tdm)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: When are randomised trials unnecessary? Picking signal from noise. https://app.cochat.ai/papers/10.1136/bmj.39070.527986.68
- The paper
- Glasziou, P., Chalmers, I., Rawlins, M., & McCulloch, P. (2007). When are randomised trials unnecessary? Picking signal from noise. BMJ. https://doi.org/10.1136/bmj.39070.527986.68
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
