The European Medical Association (EMA) defines “rare diseases” as diseases that affect fewer than 5 in 10,000 people;[1] whereas small population trials are those looking at a specified subset of the population that are of interest within a trial, such as patients with a more common ailment but who cannot be maintained effectively by current standard therapies. These types of trials are, by design, restricted in the total possible population that patients are selected from.
When performing trials on interventions for rare disease or small populations, there are often issues in being able to detect clinically important differences due to unfeasibly large sample sizes being required. There are however techniques and methods that can be utilised when setting up clinical trials to help overcome this, including some frameworks put forward by regulatory bodies (EMA and International Rare Disease Research Consortium) and a framework introduced by Parmar et al (2016) for the design of smaller population trials. [2,3]
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Without these methods, it can become hard to show with any level of rigour whether or not such a trial has successfully shown a clinically relevant level of difference between trial arms.
An interesting question is then: to what extent are these methods being used in current clinical practice?
Phastar statistician, Giles Partington, recently published a review in the Journal of Clinical Epidemiology looking at the use of methods to overcome restricted populations within rare disease and small population trials.[4] A focus was given to comparing how trials under a frequentist and Bayesian framework handled themselves.
This was a targeted review, looking at all papers since 2010 that described themselves as interventions in rare diseases or small populations. 64 trials were found to be eligible, of which 60 used frequentist methods and four used Bayesian.
It was found that frequentist trials had planned power ranging from 72-90% (median: 80%) but reported recruiting a mean of 6.6% fewer participants than planned sample size (average planned sample size n=38). Most used standard type I errors with 52 using an error rate of 5% and one using 1% error rate. The average standardised effect was high at 0.7, however 50% of trials missed this assumed level.
Of the four Bayesian trials, three used informed priors, two used priors in their design and one used it only in their analysis. Many methods were used to inform priors across these three trials with one using historical data, another using expert consensus, and the final used both. The Bayesian trials required 30%-2400% less participants than if they had used frequentist frameworks.
Although Bayesian methods were shown to offer promising solutions and were recommended as a potential solution when designing trials in rare diseases and small population settings, it was found that few trials were using this framework. Those trials that stuck to frequentist frameworks were seen to not achieve their target sample sizes and did not utilise the recommended design and analysis approaches that could have increased their statistical power.
If you would like to read more details on the review outlined, you can access Giles’ paper here: https://pubmed.ncbi.nlm.nih.gov/34910979
References
[1]European Medical Agency. Small population trials guidelines, https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-clinical-trials-small-populations_en.pdf; 2006 <European medical agency small population trialsguidelines> [accessed 14 April 2021]
[2]Institute of Medicine. Regulatory Framework for Drugs for Rare Diseases. In: Rare Diseases and Orphan Products:Accelerating Research and Development. Washington, DC: National Academies Press; 2010. p. 73-110.
[3]Parmar M, Sydes M, Morris T. How do you design randomised trials for smaller populations? A framework. BMC Med.2016; 14: 183 [4] Partington G, Cro S, Cornelius V. Design and analysis features used in small population and rare diseasetrials: A targeted review. J Clin Epidemiol. 2022 Apr; 144: 93-101
