BAYESIAN AGGREGATION OF AVERAGE DATA: AN APPLICATION IN DRUG DEVELOPMENT
BAYESIAN AGGREGATION OF AVERAGE DATA: AN APPLICATION IN DRUG DEVELOPMENT
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DOI：
10.1214/17-aoas1122
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发表时间：
2018-09-01
影响因子：
1.8
通讯作者：
Racine-Poon, Amy
中科院分区：
文献类型：
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作者：
Weber, Sebastian;Gelman, Andrew;Racine-Poon, Amy
在药物开发计划的不同阶段，随机试验用于确定候选药物的耐受性，安全性和有效性。在每个阶段，我们的目标是通过从当时可用的证据外推来优化未来研究的设计。这包括收集的试验数据和相关外部数据。然而，相关的外部数据通常仅作为平均值提供，例如，来自文献中报告的替代治疗试验。在这里，我们报告这样一个例子，从药物开发湿性年龄相关性黄斑变性。这种疾病是老年人严重视力丧失的主要原因。虽然目前的治疗选择是有效的，但它们也是患者的重大负担。因此，新的治疗方法正在开发中，需要与现有的治疗方法进行比较。这导致的一般统计问题是荟萃分析，它解决了我们如何将在不同条件下收集的数据集联合收割机结合起来的问题。贝叶斯方法长期以来一直用于实现部分池化。在这里，我们考虑当感兴趣的模型是复杂的（分层和非线性），一个数据集作为原始数据，而第二个数据集仅作为平均值给出时的挑战。在这种情况下，只有当模型对于分析方法来说足够简单时，才能应用普通的荟萃分析方法。当模型太复杂时，例如非线性，分析方法是不可能的。我们提供了一个贝叶斯的解决方案，通过使用模拟近似重建的外部总结的可能性，并允许模型中的参数在不同的条件下变化。我们首先使用假数据模拟来评估我们的方法，然后报告激发这项研究的药物开发计划的结果。
Throughout the different phases of a drug development program, randomized trials are used to establish the tolerability, safety and efficacy of a candidate drug. At each stage one aims to optimize the design of future studies by extrapolation from the available evidence at the time. This includes collected trial data and relevant external data. However, relevant external data are typically available as averages only, for example, from trials on alternative treatments reported in the literature. Here we report on such an example from a drug development for wet age-related macular degeneration. This disease is the leading cause of severe vision loss in the elderly. While current treatment options are efficacious, they are also a substantial burden for the patient. Hence, new treatments are under development which need to be compared against existing treatments.The general statistical problem this leads to is meta-analysis, which addresses the question of how we can combine data sets collected under different conditions. Bayesian methods have long been used to achieve partial pooling. Here we consider the challenge when the model of interest is complex (hierarchical and nonlinear) and one data set is given as raw data while the second data set is given as averages only. In such a situation, common meta-analytic methods can only be applied when the model is sufficiently simple for analytic approaches. When the model is too complex, for example, nonlinear, an analytic approach is not possible. We provide a Bayesian solution by using simulation to approximately reconstruct the likelihood of the external summary and allowing the parameters in the model to vary under the different conditions. We first evaluate our approach using fake data simulations and then report results for the drug development program that motivated this research.