Natural history studies (NHS) are an important resource that should be utilized during clinical trial design. NHS data provides information on the expectation and variability of potential clinical endpoints, biomarkers, and other predictors, which help explain disease progression. Modeling disease progression provides a realistic estimate of disease state, which can be leveraged along with simulation, to guide clinical trial design. This innovative approach to trial design supports a Bayesian statistical framework aimed at providing patient-centered care, while efficiently and ethically identifying effective treatments. This talk focuses on a real case example that highlights the benefits of using NHS data to design a well-powered trial in a progressive disease. Specifically, we will consider how to characterize the primary endpoint, model disease progression, augment placebo data, simulate virtual trials, and leverage innovative analysis methods to create an effective and clinically interpretable trial design.