Publications

Even in rare diseases, where the sample size is limited and blinding is less frequently implemented, randomized controlled trials are considered the gold standard to prove efficacy. We quantified how allocation bias affects the test decision in small-sample two-arm group sequential trials under a biasing policy based on the Blackwell–Hodges convergence strategy. Type I error and power were evaluated under Lan–DeMets spending, with and without futility, varying interim timing, number of looks and stage-wise restarting of randomization. Overall, group sequential choices had secondary effect and did not rescue a predictable randomization scheme. When allocation bias cannot be ruled out, we recommend less restrictive randomization procedures or, if using permuted blocks, large block sizes.

The analysis of platform trials can be enhanced by utilizing non-concurrent controls. Since including this data might also introduce bias in the treatment effect estimators if time trends are present, methods for incorporating non-concurrent controls adjusting for time have been proposed. However, so far their behavior has not been systematically investigated in platform trials that include interim analyses. To evaluate the impact of an interim analysis in trials utilizing non-concurrent controls, we consider a platform trial featuring two experimental arms and a shared control, with the second experimental arm entering later. We focus on a frequentist regression model that uses non-concurrent controls to estimate the treatment effect of the second arm and adjusts for time using a step function to account for temporal changes. We show that performing an interim analysis in Arm 1 may introduce bias in the point estimation of the effect in Arm 2, if the regression model is used without adjustment, and investigate how the marginal bias and bias conditional on the first arm continuing after the interim depend on different trial design parameters. Moreover, we propose a new estimator of the treatment effect in Arm 2, aiming to eliminate the bias introduced by both the interim analysis in Arm 1 and the time trends, and evaluate its performance in a simulation study. The newly proposed estimator is shown to substantially reduce the bias and type I error rate inflation while leading to power gains compared to an analysis using only concurrent controls.

Despite affecting nearly 30 million people in Europe and more than 300 million worldwide, rare diseases remain one of the most underserved areas of medicine. The numbers are stark: while between 6,000 and 7,000 rare conditions have been identified, the vast majority still lack targeted therapies. But why, with so many affected, are there still so few therapies for rare diseases? While scientific advances continue to identify new conditions, progress in developing and evaluating treatments has been far slower—raising difficult questions about how clinical trials are designed, conducted, and assessed.

A recent webinar series organised by the IHI-funded RealiseD project brought together over 1,150 participants—including patients, regulators, clinicians, academics, and industry representatives—to examine where rare disease clinical trials are falling short, and how they might be redesigned to better reflect biological reality, regulatory expectations, and lived experience.

The development of therapies for rare diseases (RDs) continues to face persistent challenges, including small and geographically dispersed patient populations, pronounced clinical heterogeneity, and the absence of standardized outcome measures. Basket trials—master protocol studies evaluating a single therapeutic intervention across multiple diseases linked by shared molecular or clinical characteristics—offer a promising solution to these constraints. This systematic review identified 36 basket trials targeting RDs through comprehensive searches of clinical trial registries, academic databases, and grey literature. The majority (75%) focused on rare oncological indications, with only nine trials addressing non-oncological RDs. These non-oncological studies were highly heterogeneous, spread across 25 distinct conditions without overlap, and faced persistent challenges such as the lack of validated biomarkers and standardized endpoints. Most studies (81%) were Phase II trials, highlighting the exploratory role of basket designs in early-stage development. Trial designs were predominantly nonrandomized and open-label (86%), reflecting the practical limitations of implementing rigorous methodologies in small, heterogeneous populations. The average trial duration was 6.5 years, and recruitment was logistically demanding, with trials involving a mean of 56 sites and, in some cases, over 1,000 centers. While basket trials show clear potential to accelerate therapeutic innovation in RDs, their application remains limited beyond oncology. Methodological constraints—such as inconsistent endpoints, limited randomization, and underpowered subgroup analyses—continue to restrict their broader use. Enhancing the utility of basket trials will require greater regulatory flexibility, wider adoption of adaptive and Bayesian designs, integration of real-world evidence, and stronger engagement with patients and advocacy groups. This review underscores both the opportunities and limitations of basket trials in RDs and provides a roadmap for realizing their potential, calling for concerted efforts from regulators, researchers, and patient advocates to expand their application and impact across the RDs spectrum.

This article addresses the problem of determining whether the dose response relationships between subgroups and the full population in a multiregional trial are similar. Similarity is assessed in terms of the maximal deviation between the dose response curves. We consider a parametric framework and develop two powerful bootstrap tests: one for assessing the similarity between the dose response curves of a single subgroup and that of the full population, and another for comparing the dose response curves of multiple subgroups with that of the full population. We prove the validity of these tests, investigate their finite sample properties through a simulation study and illustrate the methodology with a case study.

Complex regional pain syndrome (CRPS) is a rare, disabling pain disorder. Systematic reviews have identified a critical lack of high-quality clinical trial evidence to inform the management of CRPS. There is an urgent need to find solutions to the methodological challenges of undertaking clinical trials in CRPS. The aim of this project was to develop a methodological framework for optimising future clinical trials in CRPS (OptiMeth-CRPS).

Group sequential designs, which allow early stopping for efficacy or futility, may benefit from balanced sample sizes at interim and final analyses. This requirement for balance limits the choice of admissible randomization procedures. We investigate if the choice of randomization procedure, whether balanced or not, impacts the type I error probability and power in small sample clinical trials with group sequential designs.