标题： 贝叶斯获益风险分析 显示英文标题

标题： Bayesian Benefit Risk Analysis

摘要： 新药的审批和评估过程往往非常复杂，主要是因为需要考虑多个标准。一种标准的做法是通过效益风险分析，通常在贝叶斯框架下进行，以考虑不确定性并将数据与专家判断相结合，这通过多准则决策分析（MCDA）评分来实现。 该程序基于一个合适的模型来容纳数据的关键特征，这些数据通常是混合类型且可能存在依赖关系，因子模型提供了一个标准选择。 本文的贡献有三个方面：首先，我们扩展了现有的结构化因子模型族；其次，我们提供了一个框架来在这两者之间进行选择，该框架结合了拟合度和样本外预测性能；第三，我们提出了一种顺序估计框架，它可以带来多重好处：(i) 它允许我们在每次有新数据可用时高效地重新估算不同药物的MCDA评分，从而了解它们之间潜在波动的想法；(ii) 它可以在显而易见的结论情况下提供关于早期停止的信息，从而减少不必要的进一步接触不良治疗；(iii) 它可能动态分配治疗组基于研究目标。 顺序估计的一个缺点是计算时间增加，但可以通过有效的顺序蒙特卡洛方案来缓解，我们在本文中将其定制到贝叶斯效益风险分析的背景下。 所开发的方法论在患有II型糖尿病的患者的真实数据上进行了说明，这些患者接受了二甲双胍（MET）、罗格列酮（RSG）以及两者的组合（AVM）。 显示英文摘要

摘要： The process of approving and assessing new drugs is often quite complicated, mainly due to the fact that multiple criteria need to be considered. A standard way to proceed is with benefit risk analysis, often under the Bayesian paradigm to account for uncertainty and combine data with expert judgement, which is operationalised via multi-criteria decision analysis (MCDA) scores. The procedure is based on a suitable model to accommodate key features of the data, which are typically of mixed type and potentially depended, with factor models providing a standard choice. The contribution of this paper is threefold: first, we extend the family of existing structured factor models. Second, we provide a framework for choosing between them, which combines fit and out-of-sample predictive performance. Third, we present a sequential estimation framework that can offer multiple benefits: (i) it allows us to efficiently re-estimate MCDA scores of different drugs each time new data become available, thus getting an idea on potential fluctuations in differences between them, (ii) it can provide information on potential early stopping in cases of evident conclusions, thus reducing unnecessary further exposure to undesirable treatments; (iii) it can potentially allow to assign treatment groups dynamically based on research objectives. A drawback of sequential estimation is the increased computational time, but this can be mitigated by efficient sequential Monte Carlo schemes which we tailor in this paper to the context of Bayesian benefit risk analysis. The developed methodology is illustrated on real data on Type II diabetes patients who were administered Metformin (MET), Rosiglitazone (RSG) and a combination of the two (AVM).