The development of systematic and structured approaches to assess benefit–risk of medical products is a major challenge for regulatory decision makers. Existing benefit–risk methods depend only on the frequencies of mutually exclusive and exhaustive categories in which the subjects fall, and the responses of individuals are allowed to belong to any of the other categories during their postwithdrawal visits. In this article we introduce a semiparametric Bayesian Markov model (SBMM) that treats the withdrawal category as an absorbing state and analyzes subject-level data for multiple visits, accounting for any within-patient dependencies in the response profiles. A log-odds ratio model is used to model the subject-level effects by assuming a ratio of transition probabilities with respect to a “reference” category. A Dirichlet process is used as a semiparametric model for the subject-level effects to flexibly capture the underlying distributions of the personalized response profiles without making strong parametric assumptions. This also allows the borrowing of strength between the patients and achieves dimension reduction by allocating similar response profiles patterns into an unknown number of latent clusters. We analyze a motivating clinical trial dataset to assess the personalized benefit–risks in each arm and evaluate the aggregated benefits and risks associated with the drug Exalgo.

中文翻译：

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个人收益风险评估的半参数贝叶斯马尔可夫分析

开发用于评估医疗产品收益风险的系统和结构化方法是监管决策者面临的主要挑战。现有的利益风险方法仅取决于受试者所属的互斥和穷举类别的频率，并且在提款后访问期间允许个人的响应属于其他任何类别。在本文中，我们介绍了一种半参数贝叶斯马尔可夫模型（SBMM），该模型将戒断类别视为一种吸收状态，并针对多次就诊分析对象级别的数据，并考虑了响应配置文件中患者内部的依赖性。对数比率模型用于通过假设转移概率相对于“参考”类别的比率来对科目水平的效果进行建模。Dirichlet过程用作对象级效果的半参数模型，可以灵活地捕获个性化响应配置文件的基础分布，而无需做出强大的参数假设。这还允许在患者之间借用强度，并通过将相似的响应配置文件模式分配给未知数量的潜在簇来实现尺寸减小。我们分析了一个激励性的临床试验数据集，以评估每个部门的个性化收益风险，并评估与药物Exalgo相关的总收益和风险。这还允许在患者之间借用强度，并通过将相似的响应配置文件模式分配给未知数量的潜在簇来实现尺寸减小。我们分析了一个激励性的临床试验数据集，以评估每个部门的个性化收益风险，并评估与药物Exalgo相关的总收益和风险。这还允许在患者之间借用强度，并通过将相似的响应配置文件模式分配给未知数量的潜在簇来实现尺寸减小。我们分析了一个激励性的临床试验数据集，以评估每个部门的个性化收益风险，并评估与药物Exalgo相关的总收益和风险。