存在易出错的自我报告结果时的半参数事件时间模型--应用于妇女健康倡议
Semiparametric time to event models in the presence of error-prone, self-reported outcomes - With application to the women's health initiative
摘要：
一些无声的慢性疾病，如糖尿病的发作只能通过诊断测试来检测。出于成本考虑，在大规模前瞻性调查（如妇女健康倡议）中，定期收集自我报告的结果，以代替昂贵的诊断测试。然而，自我报告的结果受到不完美的敏感性和特异性。使用半参数似然为基础的方法，我们提出的时间到事件模型，以估计一个或多个协变量与易错，自我报告的结果的关联。我们目前的模拟研究，以评估在自我报告的结果方面的偏差，在估计的回归参数的利益的错误的影响。我们将所提出的方法应用于参加妇女健康倡议的152，830名妇女的前瞻性数据，以评估他汀类药物使用对绝经后妇女糖尿病发病风险的影响。目前的分析是基于2010年的随访，中位随访时间为12.1年。本文提出的方法很容易实现使用我们的免费R软件包icensmis，这是在综合R档案网络（CRAN）网站。
英文摘要：
The onset of several silent, chronic diseases such as diabetes can be detected only through diagnostic tests. Due to cost considerations, self-reported outcomes are routinely collected in lieu of expensive diagnostic tests in large-scale prospective investigations such as the Women's Health Initiative. However, self-reported outcomes are subject to imperfect sensitivity and specificity. Using a semiparametric likelihood-based approach, we present time to event models to estimate the association of one or more covariates with a error-prone, self-reported outcome. We present simulation studies to assess the effect of error in self-reported outcomes with regard to bias in the estimation of the regression parameter of interest. We apply the proposed methods to prospective data from 152,830 women enrolled in the Women's Health Initiative to evaluate the effect of statin use with the risk of incident diabetes mellitus among postmenopausal women. The current analysis is based on follow-up through 2010, with a median duration of follow-up of 12.1 years. The methods proposed in this paper are readily implemented using our freely available R software package icensmis, which is available at the Comprehensive R Archive Network (CRAN) website.
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