ABSTRACT

Bayesian sequential integration is an appealing approach in drug development, as it allows to recursively update posterior distributions as soon as new data become available, thus considerably reducing the computation time. However, preclinical trials are often characterized by small sample sizes, which may affect the estimation process during the first integration steps, particularly when complex PK-PD models are used. In this case, sequential integration would not be practicable, and trials should be pooled together. This work is aimed at comparing simple Bayesian pooling with sequential integration through a simulation study. The two techniques are compared under several scenarios using linear as well as nonlinear models. The results of our simulation study encourage the use of Bayesian sequential integration with linear models. However, in the case of nonlinear models several caveats arise. This paper outlines some important recommendations and precautions in that respect.

摘要

贝叶斯顺序积分是药物开发中的一种有吸引力的方法，因为它允许在新数据可用时立即递归更新后验分布，从而大大减少计算时间。然而，临床前试验的特点是样本量小，这可能会影响第一次整合步骤中的估计过程，尤其是在使用复杂的 PK-PD 模型时。在这种情况下，顺序整合是不切实际的，应该将试验集中在一起。这项工作旨在通过模拟研究将简单的贝叶斯池化与顺序集成进行比较。这两种技术在使用线性和非线性模型的几种情况下进行了比较。我们模拟研究的结果鼓励使用贝叶斯顺序集成与线性模型。然而，在非线性模型的情况下，出现了几个警告。本文概述了这方面的一些重要建议和预防措施。