Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials.
Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials.
复制标题
DOI：
10.1002/bimj.201200245
复制
发表时间：
2014-03
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
两阶段的失败者放弃设计提供了一个框架，在第一阶段选择最有前途的K实验治疗，以测试它对控制在第二阶段的验证性分析。多级失败者设计既是原始两级设计的自然延伸，也是Stallard & Friede（Stat. Med.27，6209-6227）。如果在一次中期分析后取消选择除最佳治疗外的所有治疗被认为会造成放弃真正最佳治疗的不可接受风险，则这可能是一种有用的策略。然而，这种设计还需要考虑估算。基于Cohen & Sackrowitz的工作（Stat.概率Lett. 8，273-278），我们在多级设置中得到无偏和近无偏估计。复杂的多级选择过程所造成的阻碍了一个简单的识别多级均匀最小方差条件无偏估计（UMVCUE），因此提出了两个独立的，但相关的估计，每个包含一些UMVCUE的理论特征。对于一个具体的例子，三阶段下降的失败者试验，我们比较他们的表现对几个替代估计的偏差，均方误差，置信区间宽度和覆盖范围。
The two-stage drop-the-loser design provides a framework for selecting the most promising of K experimental treatments in stage one, in order to test it against a control in a confirmatory analysis at stage two. The multistage drop-the-losers design is both a natural extension of the original two-stage design, and a special case of the more general framework of Stallard & Friede (Stat. Med. 27, 6209–6227). It may be a useful strategy if deselecting all but the best performing treatment after one interim analysis is thought to pose an unacceptable risk of dropping the truly best treatment. However, estimation has yet to be considered for this design. Building on the work of Cohen & Sackrowitz (Stat. Prob. Lett. 8, 273–278), we derive unbiased and near-unbiased estimates in the multistage setting. Complications caused by the multistage selection process are shown to hinder a simple identification of the multistage uniform minimum variance conditionally unbiased estimate (UMVCUE); two separate but related estimators are therefore proposed, each containing some of the UMVCUEs theoretical characteristics. For a specific example of a three-stage drop-the-losers trial, we compare their performance against several alternative estimators in terms of bias, mean squared error, confidence interval width and coverage.
登录
查看更多内容
影响因子：
2
作者：
Kimani, Peter K.;Todd, Susan;Stallard, Nigel
通讯作者：
Stallard, Nigel
影响因子：
2.7
作者：
EMERSON, SS;FLEMING, TR
通讯作者：
FLEMING, TR
影响因子：
2.7
作者：
WHITEHEAD, J
通讯作者：
WHITEHEAD, J
影响因子：
2.7
作者：
Liu, AY;Hall, WJ
通讯作者：
Hall, WJ
影响因子：
2
作者：
Koopmeiners, Joseph S.;Feng, Ziding;Pepe, Margaret Sullivan
通讯作者：
Pepe, Margaret Sullivan