Semiparametric ROC Curve Regression for Cancer Screening Studies

癌症筛查研究的半参数 ROC 曲线回归

• 批准号: 7501410
• 负责人: Xiaofei Wang
• 金额: $ 7.8万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-27 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7501410
• 关键词: Algorithms; Biopsy; Cancer Etiology; Cessation of life; Cohort Studies; Computer software; Condition; Data; Diagnostic; Diagnostic tests; Disease; Disease regression; Early Diagnosis; Evaluation; Genomics; Gold; Image; Invasive; Literature; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of lung; Medical Technology; Medicine; Methods; Modality; Modeling; None or Not Applicable; Patients; Performance; Personal Satisfaction; Population; Probability; Procedures; Property; Proteomics; ROC Curve; Research Personnel; Risk; Sampling; Scheme; Score; Screening for cancer; Screening procedure; Staging; Standards of Weights and Measures; Statistical Methods; Study Subject; Subgroup; Subjects Selections; Test Result; Testing; Thoracic Radiography; United States; Validation; Weight; Work; cancer diagnosis; cancer imaging; cancer regression; cost effective; data structure; design; interest; lung cancer screening; open source; prospective; simulation; user-friendly; validation studies

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.

描述(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，通过影像筛查测试，如胸部X光、CT或MRI，及早发现肺癌患者是非常有意义的。在对影像筛查测试有效性的大型验证研究中，确诊的肺癌诊断程序，如活检，太有侵入性和太昂贵，不能在所有研究对象上进行。使用有针对性的抽样计划来确定真实的疾病状况往往更符合道德或成本效益。具体地说，可以对筛查分数高的受试者进行过抽样，而对筛查分数低的受试者进行低抽样或排除。然而，这种有针对性的抽样方案会在评估筛查测试的准确性时引入验证偏差。为了完全适应抽样方案，我们开发了半参数方法来估计用于连续筛选检验的协变量特定ROC回归模型的参数。其具体目标是：(1)发展半参数经验似然方法，在观察真实疾病状态的选择概率未知或不可估量的情况下估计连续测试的协变量特有ROC曲线；(2)发展半参数经验似然方法和增广逆概率加权方法(AIPWCC)，当观察真实疾病状态的选择概率已知或可估计时，估计协变量特有ROC曲线；以及(3)开发开源软件来实现这些方法。该项目既涉及理论工作，也涉及实证工作，利用合作机会。虽然通过成像方式进行肺癌筛查是一个鼓舞人心的例子，但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，所提出的方法可能会得到广泛应用。在癌症筛查研究中，出于伦理或经济原因，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。当科目选择依赖于筛选测试的分数时，标准的统计方法将对筛选测试的准确性产生错误的评估。这在诊断医学文献中被称为验证偏差。这项研究将开发有效和一致的方法来估计连续筛查试验的协变量特有ROC曲线。它既涉及理论工作，也涉及实证工作，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，拟议中的方法可能会结束广泛的使用。