Screening tests are widely recommended for the early detection of disease among asymptomatic individuals. While detecting disease at an earlier stage has the potential to improve outcomes, screening also has negative consequences, including false positive results which may lead to anxiety, unnecessary diagnostic procedures, and increased healthcare costs. In addition, multiple false positive results could discourage participating in subsequent screening rounds. Screening guidelines typically recommend repeated screening over a period of many years, but little prior research has investigated how often individuals receive multiple false positive test results. Estimating the cumulative risk of multiple false positive results over the course of multiple rounds of screening is challenging due to the presence of censoring and competing risks, which may depend on the false positive risk, screening round, and number of prior false positive results. To address the general challenge of estimating the cumulative risk of multiple false positive test results, we propose a nonhomogeneous multistate model to describe the screening process including competing events. We developed alternative approaches for estimating the cumulative risk of multiple false positive results using this multistate model based on existing estimators for the cumulative risk of a single false positive. We compared the performance of the newly proposed models through simulation studies and illustrate model performance using data on screening mammography from the Breast Cancer Surveillance Consortium. Across most simulation scenarios, the multistate extension of a censoring bias model demonstrated lower bias compared to other approaches. In the context of screening mammography, we found that the cumulative risk of multiple false positive results is high. For instance, based on the censoring bias model, for a high-risk individual, the cumulative probability of at least two false positive mammography results after 10 rounds of annual screening is 40.4.

中文翻译：

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用于估计多个假阳性筛选测试的累积风险的非齐次马尔可夫链

筛查试验被广泛推荐用于在无症状个体中早期发现疾病。虽然在早期发现疾病有可能改善结果，但筛查也会带来负面影响，包括可能导致焦虑、不必要的诊断程序和增加医疗保健成本的假阳性结果。此外，多个假阳性结果可能会阻碍参与后续的筛选轮次。筛查指南通常建议在多年内重复筛查，但之前很少有研究调查个人接受多个假阳性检测结果的频率。由于审查和竞争风险的存在，在多轮筛选过程中估计多个假阳性结果的累积风险具有挑战性，这可能取决于假阳性风险、筛选轮次和先前假阳性结果的数量。为了解决估计多个假阳性测试结果的累积风险的一般挑战，我们提出了一个非均匀多状态模型来描述包括竞争事件的筛选过程。我们基于现有的单个误报累积风险估计器，开发了使用此多状态模型来估计多个误报结果累积风险的替代方法。我们通过模拟研究比较了新提出的模型的性能，并使用来自乳腺癌监测联盟的乳房 X 光筛查数据来说明模型性能。在大多数模拟场景中，与其他方法相比，审查偏差模型的多状态扩展显示出更低的偏差。在筛查乳房 X 光检查的背景下，我们发现多个假阳性结果的累积风险很高。例如，基于审查偏差模型，对于高风险个体，在 10 轮年度筛查后至少两次假阳性乳房 X 光检查结果的累积概率为 40.4。