发表机构
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过马尔可夫模型揭示差异检测如何扭曲基于历史协变量的风险估计，并开发逆敏感性分析框架以校正偏差，应用于前列腺癌筛查案例。
AI 中文摘要
基于历史的风险因素，如已知家族史、记录的个人史和有记录的区域史，常被视为协变量。由于这些变量依赖于检测、诊断和记录，其观测值受到检测的影响。然而，监测数据往往不可用，使得难以区分真实历史效应与检测驱动的关联。我们研究了当观测历史影响未来监测、而未来监测又影响哪些历史变为可观测时产生的反馈回路。前列腺癌筛查是一个典型案例：知晓家族史可能提高意识并增加检测，相比无已知家族史者。我们为真实和观测历史状态开发了马尔可夫模型，并表明差异检测可以扭曲观测风险比和观测历史变量的分布，甚至当真实事件风险不依赖于历史时，也能产生表面上的历史效应。当真实风险依赖于历史时，观测历史过程通常不是马尔可夫的，尽管联合真实-观测过程是。均匀的不完全检测可以通过将漏检个体转移到较不近期的观测历史状态来减弱真实历史效应。我们还开发了一个逆敏感性分析框架，结合已发表的观测关联、基线事件概率和合理的检测概率，以获得隐含的真实风险对比。数值分析说明了这些扭曲，前列腺癌家族史示例展示了使用外部校准值的敏感性计算。该框架旨在用于以记录历史为风险因素的注册、电子健康记录和监测研究。
英文摘要
History-based risk factors, such as known family history, recorded personal history, and documented regional history, are often treated as covariates. Because these variables depend on testing, diagnosis, and recording, their observed values are influenced by detection. However, monitoring data are often unavailable, making it difficult to distinguish true history effects from detection-driven associations. We study the feedback loop that arises when observed history affects future monitoring and future monitoring affects which histories become observed. Prostate cancer screening serves as a case in point: knowing a family history may raise awareness and increase testing compared with having no known history. We develop Markov models for true and observed history states and show that differential detection can distort both observed risk ratios and the distribution of the observed history variable, and can create an apparent history effect even when true event risk does not depend on history. With history-dependent true risk, the observed history process is generally not Markovian, although the joint true-observed process is. Uniform incomplete detection can attenuate true history effects by shifting individuals with missed events into less recent observed-history states. We also develop an inverse sensitivity-analysis framework that combines a published observed association, a baseline event probability, and plausible detection probabilities to obtain the implied true risk contrast. Numerical analyses illustrate the distortions, and a prostate cancer family-history example demonstrates the sensitivity calculation using external calibration values. The framework is intended for registry, electronic health record, and surveillance studies that use documented history as a risk factor.
Comments24 pages, 5 figures
