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（由申请人提供）：肺癌是美国与癌症相关死亡的主要原因。高风险人群中的胸部X射线，CT或MRI等成像筛查测试（例如胸部X射线，CT或MRI）对肺癌患者的早期发现引起了人们的极大兴趣。在大型验证研究的成像筛查测试实用性中，确定的肺癌诊断程序（例如活检）过于侵入性且昂贵，无法在所有研究对象上进行。使用有针对性的抽样方案确定真正的疾病状况通常更有道德或具有成本效益。具体而言，可能会过度筛选得分高的受试者，并且不赞成筛查评分较低的受试者。但是，这种有针对性的抽样方案将引入验证偏差以评估筛选测试的准确性。为了完全适应抽样方案，我们开发了半参数方法，以估算用于连续筛选测试的协变量特异性ROC回归模型的参数。具体的目的是（1）开发一种半参数经验可能性方法，以估计连续测试的协变特异性ROC曲线，当选择真正疾病状态的选择概率是未知或不可估量的； （2）开发一种半参数经验可能性方法和增强的反概率加权法（AIPWCC），以估计当观察真实疾病状态的选择概率是已知或可估计的，或者是可估计的； （3）开发开源软件以实现这些方法。该项目涉及理论和经验工作，借鉴协作机会。尽管通过成像方式进行肺癌筛查被用作一个激励示例，但人们对癌症和其他疾病的严格筛查测试的兴趣越来越兴趣，但提出的方法可能会发现广泛的用法。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大量受试者以确定真正的疾病状况。当受试者选择取决于筛查测试评分时，标准统计方法将对筛查测试的准确性产生错误的评估。这被称为诊断医学文献中的验证偏差。这项研究将开发有效，一致的方法，以估计连续筛选测试的协变量特异性ROC曲线。它涉及理论和经验工作，借鉴了现有的协作机会。随着对癌症和其他疾病的严格筛查测试的严格验证越来越感兴趣，建议的方法可能会结束广泛的用法。