In nonclinical pharmaceutical development, tolerance intervals are critical in ensuring product and process quality. They are statistical intervals designed to contain a specified proportion of the population with a given confidence level. Parametric and non-parametric methods have been developed to obtain tolerance intervals. The former work with small samples but can be affected by distribution misspecification. The latter offer larger flexibility but require large sample sizes. As an alternative, we propose Dirichlet process-based Bayesian nonparametric tolerance intervals to overcome the limitations. We develop a computationally efficient tolerance interval construction algorithm based on the analytically tractable quantile process of the Dirichlet process. Simulation studies show that our new approach is very robust to distributional assumptions and performs as efficiently as existing tolerance interval methods. To illustrate how the model works in practice, we apply our method to the tolerance interval estimation for potency data.

翻译：在非临床药物开发中，容忍区间对于确保产品和过程质量至关重要。它们是统计区间，旨在以给定的置信水平包含总体中指定比例的部分。为获得容忍区间，已发展出参数化和非参数化方法。前者适用于小样本，但可能受分布设定错误的影响；后者灵活性更高，但需要大样本量。作为替代方案，我们提出基于狄利克雷过程的贝叶斯非参数容忍区间，以克服这些局限。我们开发了一种计算高效的容忍区间构建算法，该算法基于狄利克雷过程在解析上可处理的量化过程。模拟研究表明，我们的新方法对分布假设具有极强的鲁棒性，且性能与现有容忍区间方法相当。为说明该模型在实际中的应用，我们将该方法应用于效价数据的容忍区间估计。