Cai Tianxi, Pepe Margaret Sullivan, Zheng Yingye, Lumley Thomas, Jenny Nancy Swords
Department of Biostatistics, Harvard University, Boston, MA 02115, USA.
Biostatistics. 2006 Apr;7(2):182-97. doi: 10.1093/biostatistics/kxi047. Epub 2005 Aug 3.
The statistical literature on assessing the accuracy of risk factors or disease markers as diagnostic tests deals almost exclusively with settings where the test, Y, is measured concurrently with disease status D. In practice, however, disease status may vary over time and there is often a time lag between when the marker is measured and the occurrence of disease. One example concerns the Framingham risk score (FR-score) as a marker for the future risk of cardiovascular events, events that occur after the score is ascertained. To evaluate such a marker, one needs to take the time lag into account since the predictive accuracy may be higher when the marker is measured closer to the time of disease occurrence. We therefore consider inference for sensitivity and specificity functions that are defined as functions of time. Semiparametric regression models are proposed. Data from a cohort study are used to estimate model parameters. One issue that arises in practice is that event times may be censored. In this research, we extend in several respects the work by Leisenring et al. (1997) that dealt only with parametric models for binary tests and uncensored data. We propose semiparametric models that accommodate continuous tests and censoring. Asymptotic distribution theory for parameter estimates is developed and procedures for making statistical inference are evaluated with simulation studies. We illustrate our methods with data from the Cardiovascular Health Study, relating the FR-score measured at enrollment to subsequent risk of cardiovascular events.
关于评估作为诊断测试的风险因素或疾病标志物准确性的统计文献几乎完全涉及测试变量Y与疾病状态D同时测量的情况。然而，在实际中，疾病状态可能随时间变化，并且在测量标志物与疾病发生之间通常存在时间滞后。一个例子是弗雷明汉风险评分（FR评分）作为心血管事件未来风险的标志物，这些事件在确定评分之后发生。为了评估这样一个标志物，需要考虑时间滞后，因为当标志物在更接近疾病发生时间测量时，预测准确性可能更高。因此，我们考虑对定义为时间函数的灵敏度和特异性函数进行推断。提出了半参数回归模型。使用队列研究的数据来估计模型参数。实际中出现的一个问题是事件时间可能被截尾。在本研究中，我们在几个方面扩展了Leisenring等人（1997年）的工作，他们的工作仅涉及二元测试的参数模型和未截尾数据。我们提出了适用于连续测试和截尾的半参数模型。发展了参数估计的渐近分布理论，并通过模拟研究评估了进行统计推断的程序。我们用心血管健康研究的数据说明了我们的方法，将入组时测量的FR评分与随后的心血管事件风险联系起来。
