Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes.

Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes.
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DOI:
10.1111/j.1541-0420.2011.01679.x
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发表时间:
2012-06
期刊:
影响因子:
1.9
通讯作者:
Liu T
Liu T
中科院分区:
数学3区
文献类型:
--
作者:
Ibrahim JG;Chen MH;Xia HA;Liu T

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美国食品药品监督管理局(FDA)最近对2型糖尿病(T2 DM)治疗新疗法的评价指南要求对心血管(CV)结局进行全项目荟萃分析。在这种情况下,我们开发了一种新的贝叶斯荟萃分析方法,使用生存回归模型来评估临床开发项目的规模是否足以评估特定的安全性终点。我们提出了一个贝叶斯样本量确定方法的荟萃分析临床试验设计的重点是控制I型错误和权力。我们还建议在将历史生存Meta数据纳入统计设计之前使用部分借用能力。所提出的方法的各种属性进行检查和一个有效的马尔可夫链蒙特卡罗抽样算法的开发后验分布的样本。此外,我们开发了一种基于模拟的算法,用于计算各种数量,如贝叶斯荟萃分析试验设计中的功效和I型错误。将拟定方法应用于2/3期开发项目的设计,包括T2 DM研究中CV风险评估的非劣效性临床试验。
Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies.
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