基于整数规划的随机化检验中二元结局错分的敏感性分析
Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming
AI总结：
针对随机化检验中二元结局错分的偏倚问题，提出无模型有限总体敏感性分析方法，定义核心量警示准确率，通过自适应重构整数规划实现高效计算，应用于PCPT试验并开发开源R包。
摘要：
在随机实验中，开展随机化检验是检验因果零假设的常用方法。随机化检验之所以广受应用，很大程度上是因为其统计有效性仅取决于随机化设计，无需对结局变量做出分布或建模假设。但随机化检验仍可能受到其他偏倚来源的影响，其中结局错分是一项重要的偏倚来源。我们提出了一种无模型的有限总体敏感性分析方法，用于处理随机化检验中的二元结局错分问题。本框架的核心量是“警示准确率（warning accuracy）”，其定义为一个阈值：若结局测量准确率未超过该阈值，基于测量结局得到的随机化检验结果可能与基于真实结局得到的结果存在差异。我们证明了在不引入额外假设的前提下，研究警示准确率及相关概念能够强化受结局错分影响的随机化检验的分析效果。我们表明，通过针对随机化设计自适应重构大规模整数规划，可在大数据集上高效计算警示准确率。我们将所提方法应用于前列腺癌预防试验（Prostate Cancer Prevention Trial, PCPT），还开发了一个开源R包用于实现该方法。
英文摘要：
Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, and no distributional or modeling assumption on the outcome variable is needed. However, randomization tests may still suffer from other sources of bias, among which outcome misclassification is a significant one. We propose a model-free and finite-population sensitivity analysis approach for binary outcome misclassification in randomization tests. A central quantity in our framework is ``warning accuracy," defined as the threshold such that a randomization test result based on the measured outcomes may differ from that based on the true outcomes if the outcome measurement accuracy did not surpass that threshold. We show how learning the warning accuracy and related concepts can amplify analyses of randomization tests subject to outcome misclassification without adding additional assumptions. We show that the warning accuracy can be computed efficiently for large data sets by adaptively reformulating a large-scale integer program with respect to the randomization design. We apply the proposed approach to the Prostate Cancer Prevention Trial (PCPT). We also developed an open-source R package for implementation of our approach.
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