Statistical Methods for Cancer Detection Using Biomarkers

使用生物标志物检测癌症的统计方法

• 批准号: 10347318
• 负责人: YIJIAN HUANG
• 金额: $ 30.59万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-11 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10347318
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Age; Biological; Biological Markers; Cancer Detection; Cancer Patient; Characteristics; Clinical; Communities; Computational algorithm; Computing Methodologies; Confidence Intervals; Data; Detection; Development; Diagnosis; Diagnostic tests; Disease; Evaluation; Formulation; Knowledge; Literature; Logic; Logistic Regressions; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Measurement; Measures; Medical; Methods; Modeling; Molecular Biology; Motivation; Names; Patient Care; Patients; Peat; Performance; Play; Population Control; Procedures; Property; Prostate; Race; Research; Role; Specificity; Statistical Methods; Technology; Testing; Therapeutic Intervention; Translating; Universities; Washington; analytical method; anticancer research; base; biomarker evaluation; cancer biomarkers; cancer care; cancer diagnosis; cancer therapy; candidate marker; case control; clinically relevant; cost; density; diagnostic accuracy; disease prognosis; experience; improved; interest; novel; precision medicine; predicting response; prostate biopsy; screening; simulation; sound; tool; user friendly software

PROJECT SUMMARY/ABSTRACT In cancer research, precision medicine hinges on the development of valid biomarkers for cancer diagnosis, dis- ease prognosis, and prediction of response to specific therapeutic interventions. Fueled by the rapid recent ad- vances in the scientific knowledge of molecular biology and high-throughput omics technologies, a large num- ber of candidate biomarkers for various cancers have been or are being identified. Statistical and computational methods play a critical role in rigorously evaluating these biomarkers and further developing clinically relevant prediction rules to ultimately improve and advance cancer treatment and patient management. However, most existing methods, for continuous biomarkers, target diagnostic accuracy measures dictated by mathematical con- venience rather than clinical utility. Particularly, a screening or diagnostic test in many clinical contexts needs to maintain a high sensitivity (or specificity) and thus specificity at a controlled sensitivity level (or sensitivity at a controlled specificity level) is a clinically desirable accuracy metric. Yet, statistical and computation methods for this metric are mostly lacking, or suboptimal even when available as in limited circumstances. To address this ur- gent analytic need, this proposed project will develop novel and efficient statistical and computational methods specifically targeting this accuracy metric of clinical interest. When a single biomarker is under consideration or compared with another biomarker, Aims 1 and 2 will provide statistical tools for the inference and for covariate adjustment. On the other hand, multiplex prediction rules that prudently combine multiple biomarkers hold the promise to achieve improved diagnostic accuracy, since many cancers are heterogeneous. For optimal multiplex rule formulation, Aims 3 and 4 will develop computation algorithms and statistical inference methods with both linear combination and, often biologically and clinically motivated, logic combinations. These proposed ana- lytic methods will be thoroughly investigated through rigorous asymptotic studies and extensive simulations. They will be applied to a number of our prostate cancer biomarker studies, which motivated this project, from the Early Disease Research Network (EDRN). User-friendly computer software will be made available to the re- search community. These proposed methods will facilitate more effective biomarker research for cancer as well as other diseases.

项目摘要/摘要 在癌症研究中，精确医学的关键在于开发有效的癌症诊断生物标志物。 减轻预后，并预测对特定fic治疗干预的反应。在最近这则快速广告的推动下- 在分子生物学和高通量组学技术的科学知识方面，有大量的fi。 许多癌症的候选生物标记物已经或正在被识别。fi。统计和计算 在严格评估这些生物标志物和进一步开发临床相关性方面，方法起着至关重要的作用 最终改善和推进癌症治疗和患者管理的预测规则。然而，大多数 现有的方法，对于连续的生物标志物，目标诊断的准确性度量由数学条件决定。 便捷性而不是临床实用性。特别是，在许多临床情况下的筛查或诊断测试需要 保持较高的敏感度(或特定fi城市)，并因此特定fi城市处于受控的敏感度级别(或敏感度在 受控规格(fi城市级别)是临床所需的准确度指标。然而，统计和计算方法对于 即使在有限的情况下可用，这一指标也大多是缺乏的，或者是次优的。为了解决这个问题- 为了满足分析需求，该项目将开发新的、高效的统计和计算方法。 SPECIfi针对的是临床关注的这一准确性指标。当考虑单一生物标记物时或 与另一个生物标志物相比，AIMS 1和2将为推断和协变量提供统计工具 调整。另一方面，谨慎结合多个生物标志物的多重预测规则持有 承诺实现更高的诊断准确率，因为许多癌症是异质性的。实现最佳多路复用 规则制定，目标3和4将开发计算算法和统计推理方法 线性组合和逻辑组合，通常是出于生物和临床的动机。这些建议的分析- 将通过严格的渐近研究和广泛的模拟来彻底研究裂解方法。 它们将被应用于我们的一些前列腺癌生物标记物研究，这些研究推动了这个项目，来自 早期疾病研究网络(EDRN)。将向再培训局提供方便使用的电脑软件。 搜索社区。这些拟议的方法也将促进更有效的癌症生物标记物研究。 和其他疾病一样。