Unbiased estimation of selected treatment means in two-stage trials
Unbiased estimation of selected treatment means in two-stage trials
复制标题
DOI：
10.1002/bimj.200810442
复制
发表时间：
2008-08-01
影响因子：
1.7
通讯作者：
Glimm, Ekkehard
中科院分区：
文献类型：
--
作者：
Bowden, Jack;Glimm, Ekkehard
当从大量潜在候选者中选择治疗方法时，在适应性临床试验中直接估计治疗效果可能会受到严重阻碍。这是因为以治疗统计数据的排名顺序为条件的选择机制会引入偏差。尽管如此，此类设计被视为快速跟踪药物开发中最有前途的化合物的实用且有效的方法。在本文中，我们扩展了 Cohen 和 Sackrowitz（1989）的方法，他们提出了对中期最佳治疗的两阶段无偏估计。这使得他们的估计能够适用于不相等的第一阶段和第二阶段样本量，并且当感兴趣的数量是 k 中最佳、第二最佳或第 j 个最佳治疗时。通过模拟探索这种新灵活性的含义。
Straightforward estimation of a treatment's effect in an adaptive clinical trial can be severely hindered when it has been chosen from a larger group of potential candidates. This is because selection mechanisms that condition on the rank order of treatment statistics introduce bias. Nevertheless, designs of this sort are seen as a practical and efficient way to fast track the most promising compounds in drug development. In this paper we extend the method of Cohen and Sackrowitz (1989) who proposed a two-stage unbiased estimate for the best performing treatment at interim. This enables their estimate to work for unequal stage one and two sample sizes, and also when the quantity of interest is the best, second best, or j-th best treatment out of k. The implications of this new flexibility are explored via simulation.