Abstract
Development of new therapeutics for a rare disease such as cystic fibrosis is hindered by challenges in accruing enough patients for clinical trials. Use of external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. Here we consider 3 statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in cystic fibrosis patients: (1) inverse probability weighting, (2) bayesian modeling with propensity-score-based power priors, and (3) hierarchical bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical-trial data setting. Simulations showed that bias in the treatment effect was less than 4% using any of the methods, with type I error (or in the bayesian cases, posterior probability of the null hypothesis) usually less than 5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases. This article is part of a Special Collection on Pharmacoepidemiology.