BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.
BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.
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DOI：
10.1002/sim.9571
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发表时间：
2022-11-20
影响因子：
2
通讯作者：
Lin, Ruitao
中科院分区：
文献类型：
--
作者：
Chi, Xiaohan;Yu, Zhangsheng;Lin, Ruitao
对于生物相似产品的监管批准，应通过严格的临床试验进行广泛的评估，以确定参考产品与建议的生物相似产品在有效性和安全性方面的相似性。现有的生物相似试验设计通常在试验监测中使用单一的一级疗效终点，然后在试验结束时的二次分析中单独评估生物相似产品的安全性。然而，在试验监测中忽视安全终点和安全性与有效性之间的相关性可能会导致较高的假阳性率，或者在早期发现安全性不同的情况下可能会延误试验的终止。我们提出了一种贝叶斯生物相似试验的最优设计，将安全性和有效性终点都纳入到一个统一的框架中。基于贝叶斯联合安全性和有效性模型，我们顺序地使用所谓的贝叶斯生物相似概率来做出去/不去的决策。我们校准了贝叶斯设计，以最大化统计功率，同时将频率I类错误率保持在标称水平。我们进行了大量的仿真研究，结果表明，该设计在误检率和平均样本量方面具有理想的性能。我们还将建议的设计应用于评估雷尼比珠单抗产品的生物相似试验。
For regulatory approval of a biosimilar product, extensive evaluations should be performed by rigorous clinical trials to establish the similarity between the reference product and the proposed biosimilar in terms of both efficacy and safety. Existing designs for biosimilar trials often use a single primary efficacy endpoint in trial monitoring, and then separately evaluate the safety of the biosimilar product in a secondary analysis at the trial completion. However, ignoring the safety endpoint and the correlation between safety and efficacy in trial monitoring may lead to a high false positive rate, or it may delay the termination of the trial when dissimilarity in safety is early detected. We propose a Bayesian optimal design for biosimilar trials by incorporating both safety and efficacy endpoints in a uniform framework. Based on a Bayesian joint safety and efficacy model, we sequentially use a so-called Bayesian biosimilar probability to make go/no-go decisions. We calibrate the Bayesian design to maximize the statistical power while maintaining the frequentist type I error rate at the nominal level. We carry out extensive simulation studies to show that the design has desirable performance in terms of the false positive rate and the average sample size. We also apply the proposed design to a biosimilar trial evaluating a ranibizumab product.
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影响因子：
1.1
作者：
Chow, Shein-Chung;Liu, Jen-pei
通讯作者：
Liu, Jen-pei
影响因子：
4.2
作者：
Ingrasciotta, Ylenia;Cutroneo, Paola M.;Trifiro, Gianluca
通讯作者：
Trifiro, Gianluca
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作者：
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通讯作者：
Jones, Byron
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作者：
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通讯作者：
Endrenyi, Laszlo
影响因子：
158.5
作者：
Rosenfeld, Philip J.;Brown, David M.;Kim, Robert Y.
通讯作者：
Kim, Robert Y.