Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Abstract

Background: Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text: Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion: The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Keywords: Adaptive; Clinical trial; External control; Meta-analytic predictive approach; Platform; Prior distribution; SMART; Small sample.

Fig. 1

Bayesian statistics provide a systematic approach to combine all available evidence. The prior illustrates knowledge known before the trial and is based on historical data from old trials, published literature, ongoing trials, and other real-world data, while the data is collected during the current clinical trial and provides the likelihood of the treatment effect. The prior and data are combined to produce the updated information or posterior distribution of the treatment effect, which is used to quantify results and infer conclusions. For example, here we see from the posterior distribution that there is 99% probability that the treatment effect is greater than 10

Fig. 2

Frequentist Operating Characteristics (type I error (left panel) and power (right panel) of proposed design with meta-analytic predictive (MAP; solid line) and robust MAP (dotted line) priors under different scenarios for mean change from baseline in PSPRS at week 52 between treatment and placebo arms (δ). When δ = 0 (left panel) there is no difference between placebo and treatment, whereas when δ = 4 (right panel) there is a treatment difference. The plot also shows the type I error (0.025, left panel) and power (0.8, right panel) for the traditional frequentist design (grey dashed line)

Fig. 3

Top panel: shows a 2 × 2 crossover design where a group of participants are randomized to a sequence of treatments to first receive treatment A then B or first receive B then A. Bottom panel: shows a small n, sequential, multiple assignment, randomized trial design with three treatment options like the ARAMIS design. R denotes randomization and A, B, C denote intervention options