Semiparametric ROC Curve Regression for Cancer Screening Studies

描述（由申请人提供）：肺癌是美国癌症相关死亡的主要原因。在高危人群中，通过影像筛查试验，如胸部x光片、CT或MRI，早期发现肺癌患者是非常有趣的。在影像学筛查试验的大型验证研究中，明确的肺癌诊断程序，如活组织检查，侵入性太强且费用昂贵，无法对所有研究对象进行。使用有针对性的抽样方案确定真实的疾病状况往往更符合伦理或更具成本效益。具体来说，可能会对筛选得分高的受试者进行过采样，而对筛选得分低的受试者进行欠采样或排除。然而，这种有针对性的抽样方案会引入验证偏差，以评估筛选试验的准确性。为了充分适应采样方案，我们开发了半参数方法来估计连续筛选试验的协变量特异性ROC回归模型的参数。具体目的是：(1)建立一种半参数经验似然法，在观察到真实疾病状态的选择概率未知或不可估计时，估计连续检验的协变量特异性ROC曲线；(2)建立半参数经验似然法和增强逆概率加权法（AIPWCC），在观察真实疾病状态的选择概率已知或可估计时，估计协变量特异性ROC曲线；(3)开发开源软件来实现这些方法。该项目涉及理论和实证工作，利用合作机会。尽管通过成像方式筛查肺癌被用作一个鼓舞人心的例子，但随着人们对癌症和其他疾病筛查试验的严格验证越来越感兴趣，所提出的方法可能会得到广泛的应用。在癌症筛查研究中，由于伦理或经济原因，通常从研究队列中选择一小部分受试者来确定真实的疾病状况。当受试者选择依赖于筛选测试分数时，标准统计方法将对筛选测试的准确性产生不正确的评估。这被称为诊断医学文献中的验证偏差。本研究将开发有效且一致的方法来估计连续筛选试验的协变量特异性ROC曲线。它涉及理论和实证工作，利用现有的合作机会。随着人们对癌症和其他疾病筛查测试的严格验证越来越感兴趣，拟议的方法可能会停止广泛使用。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer related death in the United States. Early detection of patients with lung cancer by imaging screening tests, such as chest X-rays, CT or MRI, among the high risk population is of great interest. In large validation studies for the utility of imaging screening tests, definitive lung cancer diagnosis procedures, such as biopsy, are too invasive and expensive to be undertaken on all study subjects. It is often more ethical or cost effective to ascertain the true disease status using targeted sampling schemes. Specifically, one may oversample subjects with high screening score and undersample or exclude subjects with low screening score. However, this kind of targeted sampling scheme would introduce verification bias for assessing the accuracy of screening tests. To fully accommodate the sampling schemes, we develop semi parametric methods to estimate the parameters of the covariate-specific ROC regression model for continuous screening tests. The specific aims are (1) to develop a semi parametric empirical likelihood method to estimate the covariate-specific ROC curve of continuous tests when the selection probability of observing the true disease status is unknown or inestimable; (2) to develop a semi parametric empirical likelihood method and an augmented inverse probability weighting method (AIPWCC) to estimate the covariate-specific ROC curves when the selection probability for observing the true disease status is known or estimable; and (3) to develop open source software to implement these methods. The project involves both theoretical and empirical work, drawing on collaborative opportunities. Though lung cancer screening through imaging modality is used as a motivating example, with a growing interest in rigorous validation of screening tests for cancer and other diseases, the proposed methods may find broad usage. In cancer screening studies, a fraction of subjects is often selected from the study cohort to ascertain the true disease condition due to ethical or economical reasons. When subject selection is dependent on screening test scores, standard statistical methods will yield incorrect assessment on the accuracy of the screening tests. This is known as verification bias in the literature of diagnostic medicine. This study will develop efficient and consistent methods to estimate the covariate-specific ROC curve of continuous screening tests. It involves both theoretical and empirical work, drawing on existing collaborative opportunities. With a growing interest in rigorous validation of screening tests for cancer and other diseases, the proposed methods may end broad usage.