Estimating the cumulative risk of a false-positive test in a repeated screening program
Estimating the cumulative risk of a false-positive test in a repeated screening program
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DOI：
10.1111/j.0006-341x.2004.00214.x
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发表时间：
2004-09-01
期刊：
影响因子：
1.9
通讯作者：
Kramer, BS
中科院分区：
文献类型：
--
作者：
Xu, JL;Fagerstrom, RM;Kramer, BS
筛查癌症等慢性疾病的目标是及早发现和治疗，从而降低这种疾病的死亡率。然而，筛查测试可能会产生假阳性和假阴性结果。随着筛查测试数量的增加，很明显，筛查假阳性的风险也增加了，这一发现可能会带来巨大的情感、经济和健康成本。Elmore等人。(1998年，新英格兰医学杂志338,1089-1096)，Christian等人。(2000，Journal of the National Cancer Institute 92,1657-1666)和Gelfand and Wang(2000，Statistics in Medicine 19,1865-1879)在一个有点不切实际的假设下研究了这个问题，即决定在第k次筛查中退学的决定不取决于早期k-1次筛查的结果。在本文中，我们得到了他们的假设成立的充要条件，并利用其中一个条件提供了检验该假设有效性的方法。介绍了一种新的模型，该模型不依赖于他们的假设。给出了新模型下接收假阳性筛选累积风险的极大似然估计，并证明了其渐近正态分布。文中还考虑了引入协变量信息对新模型的扩展。我们将我们的测试方法和新模型应用于大纽约健康保险计划乳腺癌筛查试验的数据。
The goal of screening tests for a chronic disease such as cancer is early detection and treatment with a consequent reduction in mortality from the disease. Screening tests, however, might produce false positive and false-negative results. With an increasing number of screening tests, it is clear that the risk of a false-positive screen, a finding with potentially significant emotional, financial, and health costs, also increases. Elmore et al. (1998, New England Journal of Medicine 338, 1089-1096), Christiansen et al. (2000, Journal of the National Cancer Institute 92, 1657-1666), and Gelfand and Wang (2000, Statistics in Medicine 19, 1865-1879) investigated this problem under the somewhat unrealistic assumption that the choice of making the decision to drop out at the kth screen does not depend upon the results of the earlier k-1 screens. In this article we obtain sufficient and necessary conditions for their assumption to hold and use one of them to provide a method for testing the validity of the assumption. A new model which does not depend on their assumption is introduced. The maximum likelihood estimator of the cumulative risk of receiving a false-positive screen under the new model is derived and its asymptotic normality is proved. The extension of the new model by incorporating covariate information is also considered. We apply our testing method and the new model to data from the breast cancer screening trial of the Health Insurance Plan of Greater New York.