pone.0336146.t008 -

Adaptive designs for integrated phase I/II trials of drug combinations are increasingly utilized to speed up the drug development process and enhance drug efficacy, particularly in the realm of cancer therapy. The model-based Continual Reassessment Method (CRM) for dose-finding is widely used to leverage accumulated data in guiding patient allocation, drawing on principles from the Bayesian framework. In this paper, we present crm12Comb, an R package we developed to streamline the Phase I/II adaptive design process for drug combinations using the CRM approach. This package supports patients’ assignment guidance in a single trial based on existing data, as well as simulation studies for conducting extensive simulations with multiple trial parameters to evaluate operating characteristics and create visual representations. It accounts for toxicity and efficacy as binary outcomes, applying partial orderings to the dose-toxicity and dose-efficacy relationships for drug combinations. crm12Comb allows for a wide range of user-specified parameters, including maximum number of patients, cohort size, drug combinations, and a variety of link functions with prior distributions, offering flexibility to accommodate diverse clinical scenarios.

药物联合疗法的整合性I/II期临床试验自适应设计，正愈发广泛地应用于加速药物研发进程并提升药物疗效，尤其在癌症治疗领域。基于模型的剂量探索持续重新评估法（Continual Reassessment Method, CRM）依托贝叶斯框架原理，通过利用已积累的临床数据指导患者分配，得到了广泛应用。本文所介绍的crm12Comb，是我们开发的一款R语言工具包，旨在通过CRM方法简化药物联合疗法的I/II期自适应设计流程。该工具包可基于已有数据为单试验中的患者分配提供指导，同时支持开展模拟研究：通过设置多组试验参数进行大规模模拟，以评估试验的操作特性并生成可视化结果。该工具包将毒性与疗效视作二分类结局，并针对药物联合疗法的剂量-毒性与剂量-疗效关系引入偏序约束。crm12Comb支持用户自定义多种参数，包括最大患者人数、队列样本量、药物联合方案，以及各类带先验分布的连接函数，可灵活适配多样化的临床场景。