ABSTRACT We develop a probability model for evaluating long-term outcomes due to regular screening that incorporates the effects of prior screening examinations. Previous models assume that individuals have no prior screening examinations at their current ages. Due to current widespread medical emphasis on screening, the consideration of screening histories is essential, particularly in assessing the benefit of future screening examinations given a certain number of previous negative screens. Screening participants are categorized into four mutually exclusive groups: symptom-free-life, no-early-detection, true-early-detection, and overdiagnosis. For each case, we develop models that incorporate a person’s current age, screening history, expected future screening frequency, screening test sensitivity, and other factors, and derive the probabilities of occurrence for the four groups. The probability of overdiagnosis among screen-detected cases is derived and estimated. The model applies to screening for any disease or condition; for concreteness, we focus on female breast cancer and use data from the study conducted by the Health Insurance Plan of Greater New York (HIP) to estimate these probabilities and corresponding credible intervals. The model can provide policy makers with important information regarding ranges of expected lives saved and percentages of true-early-detection and overdiagnosis among the screen-detected cases.

中文翻译：

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具有筛查历史的个人的长期筛查结果的推论

摘要我们开发了一种评估因常规筛查而产生的长期结果的概率模型，该模型结合了先前的筛查检查的效果。先前的模型假定个人在当前年龄没有事先筛查检查。由于当前医学对筛查的普遍重视，考虑到筛查历史至关重要，尤其是在考虑到一定数量的先前阴性筛查的情况下评估未来筛查检查的益处时。筛查参与者分为四个互斥组：无症状生活，无早期发现，真实早期发现和过度诊断。对于每种情况，我们都会开发模型，这些模型结合了一个人的当前年龄，筛查历史，预期的未来筛查频率，筛查测试的敏感性以及其他因素，并得出四组的发生概率。推导和估计筛查病例之间过度诊断的可能性。该模型适用于任何疾病或状况的筛查；具体而言，我们将重点放在女性乳腺癌上，并使用大纽约健康保险计划（HIP）进行的研究得出的数据来估算这些可能性和相应的可信区间。该模型可以为决策者提供重要信息，包括预期的挽救寿命范围以及筛查出的病例中真正及早发现和过度诊断的百分比。我们专注于女性乳腺癌，并使用大纽约健康保险计划（HIP）进行的研究得出的数据来估算这些可能性和相应的可信区间。该模型可以为决策者提供重要信息，包括预期的挽救寿命范围以及筛查出的病例中真正及早发现和过度诊断的百分比。我们专注于女性乳腺癌，并使用大纽约健康保险计划（HIP）进行的研究得出的数据来估算这些可能性和相应的可信区间。该模型可以为决策者提供重要信息，包括预期的挽救寿命范围以及筛查出的病例中真正及早发现和过度诊断的百分比。