论文标题
估计灵敏度和居民时间如何
how well can sensitivity and sojourn time be estimated
论文作者
论文摘要
慢性疾病进展模型由三个主要参数控制：灵敏度，临床前强度和逗留时间。这些参数的估计有助于优化筛选程序并检查生存的改善。存在多种方法来估计这些参数。但是，这些模型基于强大的基本假设。本文的主要目的是研究这些假设的效果。为此，我们开发了一个模拟器来模仿乳腺癌筛查计划，直接观察了疾病的精确发作和逗留时间。我们研究了假设灵敏度为恒定的，屏幕间间隔并误解了周时间的影响。我们的结果表明，估计参数之间存在很强的相关性，并且所选的寄居时间分布对估计的准确性具有很强的影响。这些发现揭示了使用相同的数据集但不同的假设来看，不同作者看似差异的结果。
Chronic disease progression models are governed by three main parameters: sensitivity, preclinical intensity, and sojourn time. The estimation of these parameters helps in optimizing screening programs and examine the improvement in survival. Multiple approaches exist to estimate those parameters. However, these models are based on strong underlying assumptions. The main aim of this article is to investigate the effect of these assumptions. For this purpose, we developed a simulator to mimic a breast cancer screening program directly observing the exact onset and the sojourn time of the disease. We investigate the effects of assuming the sensitivity to be constant, inter-screening interval and misspecifying the sojourn time. Our results indicate a strong correlation between the estimated parameters, and that the chosen sojourn time-distribution has a strong effect on the accuracy of the estimation. These findings shed a light on the seemingly discrepant results got by different authors using the same data sets but different assumptions.
