Abstract：
Development of new therapeutics for a rare disease such as cystic fibrosis (CF) is hindered by challenges in accruing enough patients for clinical trials. Using 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. We consider three statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in CF 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 <4% using any of the methods, with type 1 error (or in the Bayesian cases, posterior probability of the null hypothesis) usually <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.
摘要：
针对囊性纤维化（CF）等罕见疾病的新疗法的开发受到了为临床试验积累足够患者的挑战的阻碍。使用来自匹配良好的历史试验的外部对照可以减少前瞻性试验规模，这种方法支持了监管机构批准对其他罕见疾病的新干预措施。我们考虑了三种统计方法，将外部对照纳入减少CF患者肺部恶化的新疗法的假设临床试验中：1）逆概率加权，2)具有基于倾向得分的权力先验的贝叶斯建模，和3)具有相称先验的分层贝叶斯建模。我们通过模拟研究和真实的临床试验数据设置来比较这些方法。模拟显示，使用任何一种方法，治疗效果的偏差<4%，具有类型1错误(或在贝叶斯情况下，原假设的后验概率)通常<5%。逆概率加权对历史和预期试验人群之间协变量患病率的相似性敏感。相称的先前方法在实际临床试验数据下表现最好。在未来的临床试验中使用外部控制来减少试验规模有望实现，并可以推进罕见疾病的治疗管道。
