Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials
Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials
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DOI：
10.1093/biomet/asy004
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发表时间：
2018-06-01
期刊：
影响因子：
2.7
通讯作者：
Kimani, Peter K.
中科院分区：
文献类型：
--
作者：
Stallard, Nigel;Kimani, Peter K.
多组多阶段临床试验比较了几种实验性治疗与对照治疗，在中期分析中表现不佳的治疗下降。这导致了推理的挑战，包括无偏治疗效果估计的建设。已经提出了一些无偏条件处理选择的估计量，但特定于某些选择规则，可能忽略与对照的比较，并且不都是最小方差。我们获得估计的治疗效果相比，控制是一致的最小方差无偏的条件选择与任何指定的规则或停止徒劳。
Multi-arm multi-stage clinical trials compare several experimental treatments with a control treatment, with poorly performing treatments dropped at interim analyses. This leads to inferential challenges, including the construction of unbiased treatment effect estimators. A number of estimators which are unbiased conditional on treatment selection have been proposed, but are specific to certain selection rules, may ignore the comparison to the control and are not all minimum variance. We obtain estimators for treatment effects compared to the control that are uniformly minimum variance unbiased conditional on selection with any specified rule or stopping for futility.