Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach
Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach
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DOI：
10.1002/bimj.201700152
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发表时间：
2018-05-01
影响因子：
1.7
通讯作者：
Jones, Byron
中科院分区：
文献类型：
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作者：
Mielke, Johanna;Schmidli, Heinz;Jones, Byron
对于生物仿制药的批准，在大多数情况下，有必要在患者中进行大型III期临床试验，以使监管机构相信该产品在疗效和安全性方面与原研产品相当。由于原研药之前已经在多项试验中进行了研究，因此将该历史信息纳入等效疗效的证明中似乎是很自然的。由于生物类似药监管批准的所有研究都是确证性研究，因此要求统计方法具有合理的频率论性质，最重要的是，至少在实践中现实的所有情况下，I类错误率都得到控制。然而，众所周知，在历史数据分布与试验数据分布之间存在冲突的情况下，纳入历史信息可能导致I类错误率的膨胀。我们说明了这个问题，并确认，使用贝叶斯鲁棒元分析预测（MAP）方法作为一个例子，同时控制I型错误率在完整的参数空间和获得功率相比，标准的频率论方法，只考虑新的研究中的数据，是不可能的。我们提出了一种混合贝叶斯频率论方法的二进制端点，控制在先验分布的中心附近的I型错误率，同时提高功率。我们研究了这种方法在广泛的模拟研究的属性，并提供了一个现实世界的例子。
For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlledat least in all scenarios that are realistic in practice. However, it is well known that the incorporation of historical information can lead to an inflation of the Type I error rate in the case of a conflict between the distribution of the historical data and the distribution of the trial data. We illustrate this issue and confirm, using the Bayesian robustified meta-analytic-predictive (MAP) approach as an example, that simultaneously controlling the Type I error rate over the complete parameter space and gaining power in comparison to a standard frequentist approach that only considers the data in the new study, is not possible. We propose a hybrid Bayesian-frequentist approach for binary endpoints that controls the Type I error rate in the neighborhood of the center of the prior distribution, while improving the power. We study the properties of this approach in an extensive simulation study and provide a real-world example.