Clinical trials and rare diseases: A way out of a conundrum
Open accessAbstract
Currently, clinical trials tend to be individually funded and applicants must include a power calculation in their grant request. However, conventional levels of statistical precision are unlikely to be obtainable prospectively if the trial is required to evaluate treatment of a rare disease. This means that clinicians treating such diseases remain in ignorance and must form their judgments solely on the basis of (potentially biased) observational studies experience, and anecdote. Since some unbiased evidence is clearly better than none, this state of affairs should not continue. However, conventional (frequentist) confidence limits are unlikely to exclude a null result, even when treatments differ substantially. Bayesian methods utilise all available data to calculate probabilities that may be extrapolated directly to clinical practice. Funding bodies should therefore fund a repertoire of small trials, which need have no predetermined end, alongside standard larger studies.
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Bayesian methods are robust when sample sizes are small.
“Additionally, these Bayesian methods are robust with small sample sizes (Irony & Simon, 2006; Lilford, Thornton, & Braunholtz, 1995; Temple, 2005).”
From Integrated cognitive behavioral therapy for comorbid cannabis use and anxiety disorders: A pilot randomized controlled trial (2019) · cites this paper as Lilford, Thornton, & Braunholtz, 1995
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Quantitative Synthesis in Systematic Reviews
Lau, J., Ioannidis, J. P. A., & Schmid, C. H. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-9-199711010-00008 · Cited by 2,517 · see citing works
Clinical research for rare disease: Opportunities, challenges, and solutions
Griggs, R. C., Batshaw, M., Dunkle, M., Gopal-Srivastava, R., Kaye, E., Krischer, J., Nguyen, T., Paulus, K., & Merkel, P. A. (2009) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2008.10.003 · Cited by 401 · see citing works
Buckner, J. D., Zvolensky, M. J., Ecker, A. H., Schmidt, N. B., Lewis, E. M., Paulus, D. J., Lopez-Gamundi, P., Crapanzano, K. A., & Bakhshaie, J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.10.014 · Cited by 55 · see citing works
Lilford, R., & Stevens, A. J. (2002) · British journal of surgery · doi:10.1046/j.1365-2168.2002.01989.x · Cited by 31 · see citing works
Self-citationSchott, A. F., Welch, J. J., Verschraegen, C. F., & Kurzrock, R. (2015) · Seminars in Oncology · doi:10.1053/j.seminoncol.2015.07.010 · Cited by 20 · see citing works
- Authors
- Lilford, R. J., Thornton, J. G., & Braunholtz, D.
- Affiliations
- University of Leeds (GB)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 1995-12-16
- Volume
- 311
- Issue
- 7020
- Pages
- 1621-1625
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- PMC
- PMC2551510
- OpenAlex
- W2065646678
- Cited by
- 237
- References
- 0
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 7 · 2023: 7 · 2022: 4 · 2021: 15 · 2020: 10 · 2019: 11 · 2018: 9 · 2017: 13 · 2016: 14 · 2015: 11 · 2014: 16 · 2013: 7 · 2012: 11
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.bmj.com/company/legal-information/terms-conditions/legal-information/tdm-licencepolicy (tdm)
- Locations
- BMJ (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- rare diseases, clinical trials, trial funding, statistical power, Bayesian analysis, frequentist confidence intervals, negative results, observational studies, small trials, evidence-based medicine
- MeSH terms
- Bayes Theorem, Ethics, Medical, Humans, Randomized Controlled Trials as Topic
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