Unbiased estimation for response adaptive clinical trials.
Unbiased estimation for response adaptive clinical trials.
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DOI：
10.1177/0962280215597716
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发表时间：
2017-10
影响因子：
2.3
通讯作者：
Trippa L
中科院分区：
文献类型：
--
作者：
Bowden J;Trippa L
贝叶斯适应性试验的显著特点是，随着有关某种特定治疗方案真实价值的信息不断涌现，被随机分配到该治疗组的概率可能会发生变化。然而，在许多临床环境中，人们仍然普遍不太愿意采用这类设计。其中一个令人担忧的方面是，其基于频率学派的操作特性不佳，或者至少人们对这些特性了解甚少。我们研究了适应性随机化过程对二元结果的响应概率参数\(p\)的最大似然估计所产生的偏差。我们发现，这种偏差的幅度较小，并且在温和的假设条件下，偏差只能为负——这使得平均而言，估计值比真实值更接近零。我们得到了一个针对\(p\)的简单无偏估计量，但结果表明它的均方误差较大。因此，我们探索了两种基于逆概率加权和拉奥 - 布莱克韦尔化来提高其精度的方法。我们使用文献中两个著名的设计对这些估计策略进行了说明。
Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative – causing one’s estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao–Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature.