Approving and assessing new drugs is complex because multiple criteria must be considered simultaneously. A common approach is benefit-risk analysis, often conducted within a Bayesian framework to account for uncertainty and combine data with expert judgement, typically through multi-criteria decision analysis (MCDA) scores. This requires models that accommodate mixed and potentially correlated outcomes; latent factor models provide a natural framework. We develop a coherent Bayesian framework for benefit-risk analysis that addresses these challenges and supports sequential decision-making. We extend structured factor models to mixed outcomes and introduce a principled approach for selecting among competing specifications that combines model fit with out-of-sample predictive performance. We then develop a sequential estimation framework that updates MCDA scores as new data become available, allowing treatment comparisons to evolve over time. This supports early stopping when conclusions are clear and permits dynamic treatment allocation aligned with study objectives. To make this feasible, we develop tailored sequential Monte Carlo methods adapted to the model structure. The methodology is illustrated using data on patients with type II diabetes treated with Metformin, Rosiglitazone, and their combination.

翻译：摘要：新药审批与评估过程因需同时考量多项标准而具有复杂性。通常采用获益-风险分析的方法，该方法常在贝叶斯框架下进行，以处理不确定性并融合数据与专家判断，典型实现方式为多准则决策分析(MCDA)评分。这要求模型能够适配混合且可能相关的结局变量，而潜在因子模型为此提供了天然框架。我们构建了一个统一的贝叶斯获益-风险分析框架，既应对上述挑战，又支持序贯决策。通过将结构化因子模型扩展至混合结局，并引入一种融合模型拟合度与样本外预测性能的规范选择准则，实现竞争性模型的有序择优。继而开发序贯估计框架，可随新数据获取动态更新MCDA评分，使治疗方案的比较能随时间演进。该设计支持在结论明确时提前终止试验，并允许根据研究目标进行动态治疗分配。为保障可行性，我们定制了适配模型结构的序贯蒙特卡洛方法。通过二甲双胍、罗格列酮及其联合用药治疗的II型糖尿病患者数据，阐明了该方法的实际应用。