Industry Insight

How statistical and data analysis can support research innovations in rare diseases

Giles Partington, Lindsay Govan, Paddy O’Hara, Emily Foreman and Jennifer Visser-Rogers from Phastar consider how data can be used to improve research innovations in rare diseases
A rare disease is defined by the European Union (EU) as one that affects fewer than five in 10,000 of the general population and in the United States (US) as one that affects fewer than 200,000 of its population.There are between 5,000 and 8,000 known rare diseases; approximately 80% of rare diseases have a genetic component, 75% affect children and 30% of rare disease patients die before the age of five.2,3
A single rare disease may affect up to 30,000 people in the UK alone, meaning research into these diseases is urgently needed.1 However, research is often hindered by several factors: diagnosis is often difficult, resulting in lack of proper diagnosis or a delay in receiving a diagnosis; the population affected is sparse and spread over a wide geographical area; clinical research centres specialising in rare diseases are often limited in number and, in almost all cases, most of the patient care is provided locally.4
These issues mean that conducting clinical trials in the rare disease space is difficult. Rare disease trials are more likely to have smaller target sample sizes, be early-phase, recruit to a single arm, be non-randomised and be unblinded.4 Recent research into the treatment of rare diseases has focused on gene therapy or cell therapy. Gene therapy is particularly relevant to rare disease patients, as more than 80% of rare diseases have a known monogenic (single-gene) cause.5 Traditionally, drugs in rare diseases often work by minimising the symptoms, or managing the condition, whereas gene therapy has the potential to correct the underlying genetic defects.
Optimising data
Due to the nature of rare disease trials, it is often hard to reach necessary sample sizes to achieve appropriate powers for frequentist trials. The International Rare Diseases Research Consortium, European Medicines Association and Parmar et al (2016) have introduced frameworks to follow when designing small population trials.6
These covered several key areas: participant recruitment(considering broadening eligibility criteria, increasing recruitment timelines, expanding scope to multicentre or more international trials including working with specific disease networks); expanding the data available from participants (can patients be re-randomised? Could you perform are peatedmeasures trial? Could the design be set as a crossover, sequential or n-of-1 trial?); considering how to ensure outcomes are information rich (continuous rather than binary outcomes, larger differences between arms, stratifying patients); or considering including other available information sources through Bayesian methods.
Illustration of rare disease cells
A targeted review of rare disease and small population trials by Partington et al (2021), looking at trials from 2009 onwards, found that few studies were following these methods and that many either failed to meet their needed sample size or reduced their power to make their trials possible.7 Few decided to use Bayesian methods, which would have avoided many of these issues by enriching their trials with previous information available through historic trials, case studies or expert opinions.
Historical controls may make the recruitment for studies of rare diseases easier, by reducing required patient numbers. A 2020 publication by Ghadessi et al found that most confirmatory clinical trials using historical controls have indications in rare disease.However, using external historical controls in clinical trials involves careful analysis and skilful adjustment. Stringent methodological requirements are needed, including rigorous patient selection criteria, record of refusals (inasmuch as the intent-to-treat principle is even more important), identification of external controls in the protocol before any analysis, formalisation of statistical considerations as in a conventional randomised trial and proper selection of endpoints (response, duration of response, survival).9
Not only can the use of historical controls be useful in increasing patient numbers receiving treatment, but also could be considered necessary if the treatment is curative. If dramatic beneficial effects (eg, a cure) are likely, then it can be unethical to randomise patients within a trial to an alternative treatment so, the use of external (historical) controls is necessary. The US Food and Drug Administration (FDA) states in its guidelines that the use of natural history data as a historical comparator for patients treated in a clinical trial is often of interest, but it recognises that there are challenges associated with the use of historical controls.10 It recommends that historical controls can be used in clinical development programmes for rare diseases, comparing patients on known covariates, or in studies where the observed effect is large in comparison to variability in disease course (eg, a substantial improvement in outcome is observed with treatment in a disease that does not naturally remit). In general, the FDA states that, provided the study design permits a valid comparison, the use of historical controls may be used in limited or special circumstances.
Rare disease trials are often underfunded, and not seen as priority in comparison to more profitable disease areas.11 Progress in these trials could be expanded through work with charities due to their familiarity with networks of patients with specific diseases and their more focused look at areas regardless of profitability. Therefore, adding expertise across the industry to such projects in a pro bono setting can be vital to ensure rare diseases do not get overlooked. Similarly, an industry-wide push to ensure data has been optimised and trials are considering the available frameworks is key to ensure that trials in these areas are not wasted opportunities to produce vital research where they are most needed.
Case study
One rare disease that has seen breakthroughs in treatment due to innovative gene therapy research is adenosine deaminase severe combined immunodeficiency (ADA-SCID), an inherited metabolic disorder that causes abnormalities of the immune system and is usually diagnosed before 12 months of age.12 Patients suffering from ADA-SCID often experience failure to thrive, frequent opportunistic infections and without treatment rarely survive beyond one to two years unless immune function is restored or contact with pathogens is avoided by creating a sterile environment around the patient (the so-called ‘bubble-children’).
Historically, the most successful treatment option was haematopeic stem cell transplant (HSCT), but this is not available to most patients with ADA-SCID and often leads to complications.13 Now, gene therapy offers a single treatment option intended to cure the condition. During treatment, a correctly functioning copy of the ADA gene is introduced into haematopoietic stem cells (HSCs) that have been harvested from the patients using a gene transfer vector. These transduced cells are then returned to the patient where they initiate immune reconstitution much like HSCs from a healthy donor.14
Statistical support for gene therapy studies can include the submission for initial approval aimed at demonstrating superiority in survival over a historical control group of subjects treated with HSCT.15 Alongside comparisons to the historical control data, this study also looked at within-subject comparisons from before and after treatment. Together, these comparisons optimised the amount of information that could be generated given the limited sample size, while ensuring that all eligible patients were enrolled onto the treatment arm of the study.
Given the success of proving the efficacy of the fresh formulation of gene therapies, new studies have since focused on studying the safety and efficacy of a cryopreserved formulation. The cryopreserved option improves shelf life and allows the treatment to be produced and administered in separate locations, reducing the distance that patients need to travel to receive the treatment. One of these studies determining the safety and efficacy of this cryopreserved treatment is currently taking place at Great Ormond Street Hospital. This research is a significant contribution to the scientific community and will bring benefits to children with this life-threatening condition.
Although, by definition, rare diseases affect far fewer people compared to other conditions, their numbers are still not insignificant and these diseases can have a substantial impact on individuals and their families. Conservative estimates suggest there are 300 million people worldwide living with more than 6,000 clinically defined rare diseases (not accounting for rare cancers).16 This is between 3.5% and 5.9% of the global population at any given time.
Optimising rare disease clinical trials by enabling effective and efficient data analysis remains of paramount importance given the complexities associated with them. Biometrics expertise can be deployed to support vital research innovations, increase trial success rates and help those living with rare diseases. Continuing developments and improvements are transforming the lives of children with ADA-SCID and countless other rare diseases.
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  4. Hilgers RD, König F, Molenberghs G, Senn S. (2016) Design and analysis of clinical trials for small rare disease populations. Journal of Rare Diseases Research & Treatment. 1:53-60.
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  6. Parmar M, Sydes M, Morris T. (2016) How do you design randomised trials for smaller populations? A framework. BMC Medicine. 14: 183
  7. Partington G, Cro S, Cornelius V, et al. (2022) Design and analysis features used in small population and rare disease trials: A targeted review. J Clin Epidemiol. 144:93-101
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  9. Casali PG, Bruzzi P, Bogaerts J, et al. (2015) Rare Cancers Europe (RCE) methodological recommendations for clinical studies in rare cancers: a European consensus position paper. Annals of Oncology. 20:300‐306.
  11. Powell T, Rough E, Lewis A. (2023) Research Briefing: Patients with rare diseases. House of Commons Library
  12. Hershfield M. (2993) Adenosine Deaminase Deficiency. In: Literature Cited. University of Washington, Seattle, Seattle (WA)
  13. Hassan A, Booth C, Brightwell A, et al. (2012) Outcome of hematopoietic stem cell transplantation for adenosine deaminase– deficient severe combined immunodeficiency. Blood. 120 (17):3615–3624.
  14. Stirnadel-Farrant H, Kudari M, Garman N, et al. (2018) Gene therapy in rare diseases: the benefits and challenges of developing a patientcentric registry for Strimvelis in ADA-SCID. Orphanet J Rare Dis. 13(49) .
  15. Kohn DB, Booth C, Shaw KL, et al. (2002-2013) Autologous Ex Vivo Lentiviral Gene Therapy for Adenosine Deaminase Deficiency. NEJM. 2021; 384(21) 16. Visit:

Giles Partington is a Bayesian statistician with several years of research work into Bayesian methods, focusing on rare disease trials and Bayesian expert elicitation.

Lindsay Govan is a statistics manager with a PhD and First-Class Hons degree in Statistics. She has over 13 years’ experience as a statistician in medical research, including over seven years of experience in clinical trial development activities within CROs. Her role as a statistical lead and consultant involves leading studies for a variety of therapeutic including rare diseases.

Paddy O'Hara is a statistician at Phastar whose expertise lies in studies focused on rare diseases. With an impressive four years of experience in the clinical sector, Paddy has honed their skills in preparing regulatory submissions, ensuring adherence to stringent standards.

Emily Foreman is a statistician at Phastar with experience leading rare disease studies and an interest in research work into Bayesian methods. Emily holds an MSc in Statistics and has three years of experience in clinical trial development activities within a CRO as well as a year's experience within pharmaceutical consultancy.

Prof Jennifer Visser-Rogers is vice president, Statistical Research and Consultancy at specialist CRO, Phastar, and has a broad portfolio of achievement, particularly in the development of clinical trial methodologies. She directs the research strategy at Phastar and provides leadership and advice to statistical consultancy activities. Jen came to Phastar from the University of Oxford, where she was an associate professor and director of Statistical Consultancy Services. Jen did her BSc and MSc at Lancaster University before moving to the University of Warwick to do her PhD.