Empirical Bayes estimation of the selected treatment mean for two-stage drop-the-loser trials: a meta-analytic approach.
Empirical Bayes estimation of the selected treatment mean for two-stage drop-the-loser trials: a meta-analytic approach.
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DOI：
10.1002/sim.5920
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发表时间：
2014-02-10
影响因子：
2
通讯作者：
Glimm, Ekkehard
中科院分区：
文献类型：
--
作者：
Bowden, Jack;Brannath, Werner;Glimm, Ekkehard
在两阶段放弃失败者试验中，所选治疗的点估计并不简单，因为通过第一阶段选择过程，标准最大似然估计（MLE）中可能会产生实质性偏倚。研究一般集中在替代估计策略，适用于偏差校正的MLE，但是，这样的估计可以有一个大的均方误差。Carreras and Brannath（卡雷拉斯和布兰纳特）32：1677 - 90）最近提出在这种情况下使用一种特殊形式的收缩估计。给定一定的假设，他们的估计量在均方误差损失方面占主导地位，这为其在实践中的使用提供了一个非常有力的论据。在本文中，我们建议使用一个更一般的形式的收缩估计在下降的失败者试验，有平行的模型拟合在该地区的荟萃分析。确定了几个估计，并表现出良好的Carreras和Brannath的原始估计和极大似然估计。然而，它们需要明确估计测量治疗效应之间异质性的额外参数，或治疗效应的非常不自然的先验分布，只能在观察到第一阶段数据后指定。收缩方法是多组临床试验中准确量化治疗效果的有力工具，需要进一步研究以了解如何最大限度地发挥其效用。
Point estimation for the selected treatment in a two-stage drop-the-loser trial is not straightforward because a substantial bias can be induced in the standard maximum likelihood estimate (MLE) through the first stage selection process. Research has generally focused on alternative estimation strategies that apply a bias correction to the MLE; however, such estimators can have a large mean squared error. Carreras and Brannath (Stat. Med. 32:1677-90) have recently proposed using a special form of shrinkage estimation in this context. Given certain assumptions, their estimator is shown to dominate the MLE in terms of mean squared error loss, which provides a very powerful argument for its use in practice. In this paper, we suggest the use of a more general form of shrinkage estimation in drop-the-loser trials that has parallels with model fitting in the area of meta-analysis. Several estimators are identified and are shown to perform favourably to Carreras and Brannath's original estimator and the MLE. However, they necessitate either explicit estimation of an additional parameter measuring the heterogeneity between treatment effects or a quite unnatural prior distribution for the treatment effects that can only be specified after the first stage data has been observed. Shrinkage methods are a powerful tool for accurately quantifying treatment effects in multi-arm clinical trials, and further research is needed to understand how to maximise their utility.
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