The use of modeling to understand the impact of screening on US mortality: examples from mammography and PSA testing
The use of modeling to understand the impact of screening on US mortality: examples from mammography and PSA testing
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DOI：
10.1191/0962280204sm376ra
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发表时间：
2004-01-01
影响因子：
2.3
通讯作者：
Mariotto, A
中科院分区：
文献类型：
--
作者：
Feuer, EJ;Etzioni, R;Mariotto, A
监测数据是了解癌症控制干预措施对人口癌症负担影响的重要资源。然而，人口癌症趋势是许多因素的复杂产物，估计其中任何一个因素的贡献都可能具有挑战性。监测建模是一种用于估计一种或多种感兴趣的干预措施对疾病发病率和死亡率趋势的贡献的技术。在这篇文章中，我们提出了几种方法来监测癌症筛查干预建模。我们将模型分类为生物学或流行病学，这取决于它们是否模拟了疾病发作和进展的全部不可观察方面，或者通过使用大多数观察到的人群水平测量来总结疾病过程的部分来将复杂过程简化为简单术语的模型。我们还描述了宏观层次的模型，微观模拟模型和机理模型之间的差异。我们讨论了模型校准和验证的程序，以及提出模型结果的方法，这些结果对于某些类型的有偏模型估计是稳健的。作为例子，我们提出了几个模型的影响，乳房X线摄影筛查乳腺癌死亡率，PSA筛查前列腺癌死亡率。这两个例子都是监测模型的适当用途，即使对于乳房X光检查有广泛的（尽管有些争议）随机试验证据，而对于PSA，这种生物标志物在其有效性的任何对照试验证据之前已被广泛用作筛选试验。这里介绍的一些模型是作为国家癌症研究所癌症干预和监测建模网络的一部分开发的。
Surveillance data represent a vital resource for understanding the impact of cancer control interventions on the population cancer burden. However, population cancer trends are a complex product of many factors, and estimating the contribution of any one of these factors can be challenging. Surveillance modeling is a technique for estimating the contribution of one or more interventions of interest to trends in disease incidence and mortality. In this article, we present several approaches to surveillance modeling of cancer screening interventions. We classify models as biological or epidemiological, depending on whether they model the full unobservable aspects of disease onset and progression, or models which reduce the complex process to simpler terms by summarizing portions of the disease process using mostly observed population level measures. We also describe differences between macrolevel models, microsimulation models and mechanistic models. We discuss procedures for model calibration and validation, and methods for presenting model results which are robust with respect to certain types of biased model estimates. As examples, we present several models of the impact of mammography screening on breast cancer mortality, and PSA screening on prostate cancer mortality. Both these examples are appropriate uses of surveillance modeling, even though for mammography there is extensive (although somewhat controversial) randomized trial evidence, whereas for PSA this biomarker has seen extensive use as a screening test prior to any controlled trial evidence of its efficacy. Some of the models presented here were developed as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network.