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Keywords: Bayesian, Disease Progression Model, MPS IIIA, Rare Disease, Pediatric Disease, Single Arm Trial, Natural History Control
Rare diseases present a variety of challenges for traditional study designs. These difficulties include limited patient populations, heterogeneous disease development, and an urgency to treat that makes randomization to placebo difficult. We present a clinical trial design that addresses these issues for studies in Mucopolysaccharidosis (MPS) IIIA, a rare genetic disease that leads to neurodegeneration in children. This design uses a Bayesian disease progression model (DPM), informed by natural history data, to characterizes the cognitive decay of children with MPS IIIA. This model can be adapted for clinical trials to estimate the rate at which an investigational therapy slows disease progression relative to the natural history. A primary advantage of this approach is that the analysis accounts for key confounders in this disease such as chronological age, disease heterogeneity, treatment age, and length-of-follow up. The combination of natural history data and DPM facilitate the design of small, efficient single-arm trials in MPS IIIA that are appropriate when a large treatment effect is anticipated. Clinical trial simulation is used to show that the DPM analysis can reduce study sample sizes by up to 50% relative to traditional analysis methods.