A conditioning tactic that increases design sensitivity in observational block designs
提高观察性区组设计敏感度的条件化策略AI 解读 ↓
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提高观察性区组设计敏感度的条件化策略
- 研究问题
- 观察性区组研究未随机分配处理时，怎样提高敏感性分析区分真实处理效应与未观测处理分配偏差的能力？
- 方法与思路
- 作者对响应极差中等偏大的区组使用条件统计量，条件于处理个体响应为组内最大或最小，并作渐近、模拟和实例分析。
- 材料陈述的贡献
- 摘要称，该策略进一步提高设计敏感度，并报告有限样本敏感性分析功效；文中所述 R 包包含方法、数据及复现功能。
- 阅读关注点
- 应核对隐藏偏差模型、区组筛选和条件化规则、设计敏感度比较的条件，以及自适应推断如何保持检验有效性。
- 阅读边界
- 仅据所给 Crossref 摘要；敏感度增益条件、功效数值与实例细节未说明，敏感性分析本身也不能证明不存在未观测偏差。
原文摘要
Abstract In an observational block design, there are I blocks of J individuals, typically with one treated individual and J−1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It is known that weighting to ignore blocks with a small range of responses increases the ability to distinguish a treatment effect from a bias in treatment assignment—that is, it increases the design sensitivity. Here, it is shown that a new tactic further increases design sensitivity. The new tactic involves a conditional statistic, such that blocks with moderately large ranges are considered conditionally given that the treated individual has either the largest or smallest response in the block. The new tactic is explored: (i) in terms of an asymptotic measure, the design sensitivity, (ii) in simulation of the power of a sensitivity analysis in finite samples, and (iii) in an example. Adaptive inference is briefly discussed. An R package weightedRank implements the method, contains the data, and reproduces the empirical results.