随机临床试验中昂贵结局的自适应抽样
Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials
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- University of Louisville（路易斯维尔大学）
- University of Louisville School of Medicine（路易斯维尔大学医学院）
- Takeda Pharmaceuticals（武田制药）
- Bristol Myers Squibb（百时美施贵宝）
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中文总结 AI 辅助
针对随机试验中昂贵结局，提出盲态自适应抽样设计，用增强逆概率加权估计，模拟显示比简单随机抽样效率高12%-34%，方差降低39%。
中文摘要 AI 辅助
在一些随机试验中，主要结局的测量成本高昂或耗时较长，而能够预测该结局的辅助变量对所有受试者均可获得。此时，可以在概率样本中测量结局。我们研究了一种自适应设计，其中选择测量结局的统计学家对治疗分配保持盲态。来自初始随机样本的结局被用于拟合结局及其残差方差的合并模型。这些模型为剩余参与者设定抽样概率，并可在结局累积过程中重新拟合。在揭盲后，通过增强逆概率加权估计各组的均值。对于任何工作模型，该估计量对完整数据的治疗差异都是无偏的，并且鞅中心极限定理在重复更新下给出Wald区间。我们限制了因估计抽样规则而损失的方差。该界限与拟合残差方差的误差呈线性关系，当最优概率未被截断时呈二次关系，并将初始样本量与模型的学习速率联系起来。相对于使用治疗分配的设计，盲态设计因两臂残差方差不相等而损失一项，并因条件治疗效应而损失一项，后者在零假设附近为二阶小量。在模拟中，覆盖率接近名义水平。自适应抽样比简单随机抽样效率高12%至34%，在相同精度下所需测量的结局数量减少10%至26%。在一项抗真菌试验的重采样研究中，自适应抽样将抽样方差降低了39%。
英文摘要
In some randomized trials the primary outcome is costly or slow to measure, while auxiliary variables that predict it are available for everyone. The outcome can then be measured in a probability sample. We study an adaptive design in which the statistician who selects outcomes to measure is blinded to treatment assignment. Outcomes from an initial random sample are used to fit pooled models for the outcome and its residual variance. These set the sampling probabilities for the remaining participants and may be refitted as outcomes accumulate. After unblinding, arm means are estimated by augmented inverse probability weighting. The estimator is unbiased for the complete-data treatment difference for any working models, and a martingale central limit theorem gives Wald intervals under repeated updating. We bound the variance lost by estimating the sampling rule. The bound is linear in the error of the fitted residual variance, quadratic when the optimal probabilities are not truncated, and relates the initial sample size to the learning rate of the models. Relative to designs using treatment assignment, the blinded design loses a term due to unequal residual variances in the two arms and a term due to the conditional treatment effect, which is second order near the null. In simulations, coverage was near nominal. Adaptive sampling was 12% to 34% more efficient than simple random sampling and needed 10% to 26% fewer measured outcomes for the same precision. In a resampling study of an antifungal trial, adaptive sampling reduced the sampling variance by 39%.
