A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?
A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?
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DOI：
10.1016/j.cct.2011.03.010
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发表时间：
2011-07-01
影响因子：
2.2
通讯作者：
Satariano, William A.
中科院分区：
文献类型：
--
作者：
Pressman, Alice R.;Avins, Andrew L.;Satariano, William A.
背景：很少有文献将贝叶斯分析技术与使用真实试验数据分析临床试验的传统方法进行比较。方法：我们使用两项已发表的临床试验的数据比较了贝叶斯分析法和频率论组序贯法。我们选择了两个广泛接受的频率论规则，O'Brien-Fleming 和 Lan-DeMets，以及共轭贝叶斯先验。使用非参数引导程序，我们估计了每种方法的停止时间的抽样分布。由于当前的实践要求保留实验误报率（I 类错误），因此我们使用在模拟空样本中检测效果的后验概率来近似贝叶斯分析和频率分析的这些错误率。因此，对于这些试验所代表的数据生成分布，我们能够比较这些技术的相对性能。结果：最终结果与原始试验的结果没有不同。然而，试验终止的时间因方法和试验而异。对于一项试验，任一类型的分组序贯设计都要求提前停止研究。另一方面，停止时间取决于支出函数和先验分布的选择。结论：结果表明，除了传统的临床试验分析方法之外，试验者还应该考虑贝叶斯方法。尽管这个小样本的研究结果并未证明任何一种方法始终优于另一种方法，但他们确实表明需要使用来自不同临床试验的数据来重复这些比较，以确定不同方法最有效的条件。 (C) 2011 Elsevier Inc. 保留所有权利。
Background: There is a paucity of literature comparing Bayesian analytic techniques with traditional approaches for analyzing clinical trials using real trial data.Methods: We compared Bayesian and frequentist group sequential methods using data from two published clinical trials. We chose two widely accepted frequentist rules, O'Brien-Fleming and Lan-DeMets, and conjugate Bayesian priors. Using the nonparametric bootstrap, we estimated a sampling distribution of stopping times for each method. Because current practice dictates the preservation of an experiment-wise false positive rate (Type I error), we approximated these error rates for our Bayesian and frequentist analyses with the posterior probability of detecting an effect in a simulated null sample. Thus for the data-generated distribution represented by these trials, we were able to compare the relative performance of these techniques.Results: No final outcomes differed from those of the original trials. However, the timing of trial termination differed substantially by method and varied by trial. For one trial, group sequential designs of either type dictated early stopping of the study. In the other, stopping times were dependent upon the choice of spending function and prior distribution.Conclusions: Results indicate that trialists ought to consider Bayesian methods in addition to traditional approaches for analysis of clinical trials. Though findings from this small sample did not demonstrate either method to consistently outperform the other, they did suggest the need to replicate these comparisons using data from varied clinical trials in order to determine the conditions under which the different methods would be most efficient. (C) 2011 Elsevier Inc. All rights reserved.