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作者：Gui, Lin; Jiang, Yuchao; Wang, Jingshu
作者单位：University of Chicago; Texas A&M University System; Texas A&M University College Station
摘要：Combining dependent $ p $-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, $ p $-value combination tests based on regularly-varying-tailed distributions, such as the Cauchy combination test and harmonic mean $ p $-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of these methods under an asympto...
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作者：Gao, Chenyin; Yang, Shu; Shan, Mingyang; Ye, Wenyu; Lipkovich, Ilya; Faries, Douglas
作者单位：North Carolina State University; Eli Lilly; Lilly Research Laboratories
摘要：In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external control...
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作者：Qu, Tianyi; Du, Jiangchuan; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago
摘要：Randomized experiments have been the gold standard for drawing causal inference. The conventional model-based approach has been one of the most popular methods of analysing treatment effects from randomized experiments, which is often carried out through inference for certain model parameters. In this paper, we provide a systematic investigation of model-based analyses for treatment effects under the randomization-based inference framework. This framework does not impose any distributional ass...
