Statistical Methods for Selection and Evaluation of Biomarkers

生物标志物选择和评价的统计方法

• 批准号: 8660307
• 负责人: Ying Huang
• 金额: $ 32.44万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-10 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8660307
• 关键词: Address; Algorithms; Area; Biological Markers; Case-Control Studies; Characteristics; Classification; Data; Detection; Development; Diagnosis; Diagnostic tests; Disease; Disease Outcome; Early Detection Research Network; Evaluation; General Population; Goals; Individual; Individual Differences; Laboratories; Linear Models; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Medical; Methods; Modeling; Outcome; Pancreas; Patients; Performance; Population; ROC Curve; Research; Research Design; Risk Factors; Sampling; Scheme; Science; Selection for Treatments; Sensitivity and Specificity; Southwest Oncology Group; Specific qualifier value; Staging; Statistical Methods; Statistical Models; Testing; Time; Treatment Cost; Women' s Health; base; burden of illness; cancer genome; case control; clinical practice; cohort; cost; design; disease classification; disease diagnosis; disorder risk; genome wide association study; improved; interest; malignant breast neoplasm; novel; patient population; programs; public health relevance; randomized trial; response; screening; tool; treatment effect

DESCRIPTION (provided by applicant): Recent advances in the laboratory sciences have led to the discovery of a large number of candidate biomarkers, which hold great potential for disease diagnosis and treatment. At this time, an important research bottleneck is the lack of well-developed statistical methods for effectively using these candidate biomarkers to enhance clinical practice. It is our goal to develop new tools to select, combine, and evaluate biomarkers for disease classification and treatment selection. Classification markers predict an individual's disease outcome and are useful for the detection of diseases at an early stage when a treatment is most effective. Research proposed in Aim 1 seeks to select and combine markers to improve the classification performance in disease screening and diagnosis. Treatment selection markers predict a patient's response to different therapies and allow for the selection of a therapy that has the best predicted outcome. Aim 2 seeks to develop marker-based treatment selection rules to maximize the benefit to the patient population. A biomarker that is useful for guiding treatment decision to the general population will have different values to different patients due to individual differences in their response to treatment and in their tolerance of the disease harm and treatment cost. Aim 3 seeks to develop a new graphical tool to customize the evaluation of a biomarker for aiding treatment decision based on personal characteristics. Our statistical methods will apply broadly to general medical fields. In particulr, we will apply these methods to analyze several cancer studies including (1) biomarker studies for prostate cancer and pan- creatic cancer from the Early Detection and Research Network; (2) the Women's Health Initiative breast cancer genome-wide association study; and (3) the Oncotype-Dx breast cancer study from the Southwest Oncology Group. Programs and algorithms developed in this proposal will be made available to public.

描述（由申请人提供）：实验室科学的最新进展导致发现了大量候选生物标志物，这具有疾病诊断和治疗的巨大潜力。目前，一项重要的研究瓶颈是缺乏有效使用这些候选生物标志物来增强临床实践的发达统计方法。我们的目标是开发新的工具来选择，组合和评估用于疾病分类和治疗选择的生物标志物。分类标记可以预测一个人的疾病结果，对于在治疗最有效的早期阶段检测疾病很有用。 AIM 1中提出的研究旨在选择和组合标记，以提高疾病筛查和诊断的分类表现。治疗选择标志物预测患者对不同疗法的反应，并允许选择具有最佳预测结果的疗法。 AIM 2试图制定基于标记的治疗选择规则，以最大程度地利用患者人群的好处。一种生物标志物，可用于指导对普通人群的治疗决策，因为他们对治疗的反应以及对疾病的危害和治疗成本的耐受性的个体差异，对不同患者的价值观会有所不同。 AIM 3试图开发一种新的图形工具，以根据个人特征来定制生物标志物的评估，以帮助治疗决策。 我们的统计方法将广泛应用于一般医疗领域。在特定于众所周知，我们将应用这些方法分析几项癌症研究，包括（1）从早期检测和研究网络中研究前列腺癌和泛云虫癌的生物标志物研究； （2）妇女健康倡议乳腺癌全基因组协会研究； （3）西南肿瘤学组的Oncotype-DX乳腺癌研究。该提案中开发的程序和算法将向公众提供。