Confidence intervals for the selected population in randomized trials that adapt the population enrolled.
Confidence intervals for the selected population in randomized trials that adapt the population enrolled.
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DOI：
10.1002/bimj.201200080
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发表时间：
2013-05
影响因子：
1.7
通讯作者：
Rosenblum, Michael
中科院分区：
文献类型：
--
作者：
Rosenblum, Michael
当怀疑某种治疗方法可能仅对目标人群的某些子集有益时，设计随机试验是一项挑战。在这种情况下，已提出试验设计，根据中期分析以预先计划的方式修改招募的人群。例如，如果试验期间有早期证据表明该治疗仅对特定人群有益，则入组可能仅限于该人群。在这样的试验结束时，需要对所选人群进行推断。我们专注于构建所选人群的平均治疗效果的置信区间。未能考虑设计的自适应性质的置信区间方法可能无法获得所需的覆盖概率。我们提供了一种新的过程，用于构建具有至少 95% 覆盖概率的置信区间，均匀地覆盖一大类可能的数据生成分布。我们的方法涉及计算标准置信区间必须扩展的最小因子 c，以便渐近地均匀地具有至少 95% 的覆盖概率。计算扩展因子 c 并不是微不足道的，因为对于给定的决策规则，哪种数据生成分布会导致最坏情况的覆盖概率并不是先验清楚的。我们给出了计算 c 的算法，并证明了所得置信区间过程的最优性属性。
It is a challenge to design randomized trials when it is suspected that a treatment may benefit only certain subsets of the target population. In such situations, trial designs have been proposed that modify the population enrolled based on an interim analysis, in a preplanned manner. For example, if there is early evidence during the trial that the treatment only benefits a certain subset of the population, enrollment may then be restricted to this subset. At the end of such a trial, it is desirable to draw inferences about the selected population. We focus on constructing confidence intervals for the average treatment effect in the selected population. Confidence interval methods that fail to account for the adaptive nature of the design may fail to have the desired coverage probability. We provide a new procedure for constructing confidence intervals having at least 95% coverage probability, uniformly over a large class of possible data generating distributions. Our method involves computing the minimum factor c by which a standard confidence interval must be expanded in order to have, asymptotically, at least 95% coverage probability, uniformly over . Computing the expansion factor c is not trivial, since it is not a priori clear, for a given decision rule, which data generating distribution leads to the worst-case coverage probability. We give an algorithm that computes c, and prove an optimality property for the resulting confidence interval procedure.
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影响因子：
2.7
作者：
Rosenblum, M.;van der Laan, M. J.
通讯作者：
van der Laan, M. J.
影响因子：
15.8
作者：
Kirsch, Irving;Deacon, Brett J.;Huedo-Medina, Tania B.;Scoboria, Alan;Moore, Thomas J.;Johnson, Blair T.
通讯作者：
Johnson, Blair T.
影响因子：
1.7
作者：
Wang, Sue-Jane;Hung, H. M. James;O'Neill, Robert T.
通讯作者：
O'Neill, Robert T.
影响因子：
2.3
作者：
GOTZE, F
通讯作者：
GOTZE, F
影响因子：
1.5
作者：
Wang, Sue-Jane;O'Neill, Robert T.;Hung, H. M. James
通讯作者：
Hung, H. M. James