Multiple conditional randomization tests for lagged and spillover treatment effects
提出用单个数据集构建多个独立条件随机化检验的方法，可分别解读p值并用多重检验方法合并，应用于观察性研究的证据因子、阶梯楔形试验的滞后效应和存在干扰的随机试验的溢出效应。
Abstract We consider the problem of constructing multiple independent conditional randomization tests using a single dataset. Because the tests are independent, the randomization p-values can be interpreted individually and combined using standard methods for multiple testing. We give a simple, sequential construction of such tests and then discuss its application to three problems: Rosenbaum’s evidence factors for observational studies, lagged treatment effects in stepped-wedge trials, and spillover effects in randomized trials with interference. We compare the proposed approach with some existing methods using simulated and real datasets. Finally, we establish a more general sufficient condition for independent conditional randomization tests.