发表机构
Pennsylvania State University; Columbia University Medical Center; University of North Carolina at Chapel Hill（宾夕法尼亚州立大学; 哥伦比亚大学医学中心; 北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对右删失协变量下保形预测区间变异性大的问题，提出半参数预测方法，利用分布信息估计半长，在高删失率下实现稳定区间长度与可靠覆盖率，优于保形预测。
AI 中文摘要
在队列研究中，当资源密集型测量被跳过，患者就诊时有时无法测量临床结局，而预测区间提供了未测量结局可能的合理取值范围，并以预测水平报告。构建该区间需要知晓患者疾病进展程度，而事件时间协变量（即所有患者共有的锚定事件发生时间）可将每个患者置于疾病的可比节点。然而，对于许多患者，该事件在随访结束时仍未发生，因此事件时间协变量呈右删失：其值未被观测到，仅已知超过研究退出时间。保形预测方法可适配此右删失协变量场景，但这些方法生成的区间长度和覆盖率在不同研究间差异显著。这种变异性无法支持可靠的临床决策，因为结局范围会过度依赖研究样本而非真实疾病过程。我们提出一种半参数预测方法，将预测区间的构建转化为其半长的半参数估计，利用保形预测方法丢弃的分布信息。该方法在估计半长时达到最小可能方差，且即使事件时间协变量模型或删失时间模型设定错误，仍保持一致性。模拟研究证实，在不同删失率下，该方法相比保形预测方法的区间长度和覆盖率显著更稳定。在一项删失率为77.2%的亨廷顿舞蹈症研究中，我们的方法实现了可靠的覆盖率和稳定的区间长度，而保形预测方法要么持续出现覆盖率不足，要么区间过宽而不具参考价值。
英文摘要
In clinical studies, a patient's outcome, e.g., a cognitive test score, is typical or atypical depending on how it compares with the outcomes of patients at a similar point in a neurodegenerative disease, measured by how far they are from a common disease event. A prediction interval based on the time to that event provides that comparison, but that time is right-censored for most patients. Yet no method had computed such a prediction interval in this right-censored covariate setting. We first adapt three conformal prediction methods to this setting, and show that the estimated half-length, the distance the interval runs either side of its center, varies from one study to the next, so the same outcome can be judged typical in one study and atypical in another. We then develop the PRESCCO method, whose estimator of the half-length is semiparametrically efficient and doubly robust, staying consistent when one of its two models is misspecified, while the method loses no coverage. In simulations its standard deviation is four to thirty times smaller than under any conformal prediction method, and in a Huntington disease study with 77.2% right-censoring, an outcome typical in one study is no longer atypical in another.
Comments57 pages, 5 figures
