Statistical Methods for Cancer Biomarkers

癌症生物标志物的统计方法

• 批准号: 8253824
• 负责人: Debashis Ghosh
• 金额: $ 25.84万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8253824
• 关键词: Address; Biological Markers; Cancer Science; Clinical; Clinical Trials; Data; Data Analyses; Data Set; Decision Making; Detection; Drug Formulations; Evaluation; Genes; Goals; Joints; Knowledge; Least-Squares Analysis; Literature; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Metric; Modeling; Monitor; Nature; Outcome; Patients; Prevention; Proteins; Randomized Clinical Trials; Research; Research Design; Risk; Risk Factors; Sampling; Scheme; Simulate; Source; Statistical Methods; Subgroup; Surrogate Endpoint; Symptoms; Technology; Therapy Evaluation; Uncertainty; Validation; anticancer research; frailty; improved; indexing; innovation; interest; novel; oncology; predictive modeling; prognostic; randomized trial; simulation; surrogacy

DESCRIPTION (provided by applicant): Biomarkers in cancer research are considered a central component of the expected improvements in prevention, detection, treatment and monitoring. There are potentially useful in many different types of studies and for many different purposes. Critical questions are whether they are valid to use, how can they be utilized in a valid and efficient way, and then if they are used how confident is one in the conclusions that are obtained. The use of biomarkers to advance understanding in cancer science has great potential, but also has some risks. Biomarkers are subject to uncertainty in their measurement, they may not be measuring exactly the quantity of interest, and since they are not explicitly measures of symptoms their use to aid in decision making or evaluation of therapies in a clinical setting is subject to uncertainty. Thus careful analysis of data from studies that involve biomarkers is crucial. There are many statistical challenges that arise in such studies. This application is concerned with developing, evaluating and applying statistical methods for data that involves biomarkers. The first aim is concerned with adding biomarkers to prediction models that may be used to stratify or classify patients. In this aim we develop approaches for integrating data from other sources to improve the prediction models. This research will have broad applicability. Innovative aspects involve the use of targeted ridge regression, multi-kernel machine modeling, and importance sampling to incorporate information from the literature. The second aim is concerned with clinical trials where the biomarker is to be used to evaluate a therapy as a surrogate endpoint. Because of the nature of the scientific question causal modeling is very natural in this context. We propose to develop both potential outcomes and structural causal models. We will investigate both single trial and multi trial settings with different endpoint types. The third aim is concerned with therapies that may be effective only for a subgroup of patients, and to be useful this subgroup is determined by a small number of predictive biomarkers. For data from randomized clinical trials we suggest a unified modeling approach, and will investigate the use of single index models with variable selection and multivariate partial least squares to aid in the subgroup identification. Inference following subgroup identification is challenging, we suggest an innovative scheme to simulate data under an appropriate null distribution. All 3 aims in this proposal address fundamental and significant problems in translational oncology research. Successful completion of the aims will have an impact both in understanding and utilizing biomarkers and also in developing statistical methodology that can be more broadly applicable to other fields. PUBLIC HEALTH RELEVANCE: Biomarkers are considered a central component of the expected improvements in prevention, detection, treatment and monitoring in cancer. Critical questions about biomarkers are when and whether they are valid to use, how can they be utilized in a valid and efficient way, and then if they are used how confident is one in the conclusions that are obtained. This proposal is concerned with developing proper and efficient statistical methods for evaluation of biomarker data.

描述（由申请人提供）：癌症研究中的生物标志物被认为是预防、检测、治疗和监测方面预期改进的核心组成部分。在许多不同类型的研究和许多不同的目的中有潜在的用处。关键问题是它们是否有效使用，如何以有效和有效的方式使用它们，以及如果使用它们，那么对所获得的结论有多大的信心。使用生物标志物来促进癌症科学的理解具有巨大的潜力，但也存在一些风险。生物标志物在其测量中受到不确定性的影响，它们可能无法精确测量感兴趣的量，并且由于它们不是症状的明确测量，因此它们用于辅助临床环境中的治疗决策或评估受到不确定性的影响。因此，仔细分析涉及生物标志物的研究数据至关重要。在这些研究中出现了许多统计挑战。该应用程序涉及开发，评估和应用涉及生物标志物的数据的统计方法。 第一个目标是将生物标志物添加到可用于对患者进行分层或分类的预测模型中。在这个目标中，我们开发的方法，从其他来源的数据整合，以改善预测模型。这项研究将具有广泛的适用性。创新方面涉及使用有针对性的岭回归，多核机器建模，并将信息从文献的重要性抽样。 第二个目标是与临床试验有关，其中生物标志物将用于评估作为替代终点的治疗。由于科学问题的性质，因果建模在这种情况下是非常自然的。我们建议开发潜在的结果和结构因果模型。我们将研究具有不同终点类型的单项试验和多项试验设置。 第三个目标是关于可能仅对患者亚组有效的疗法，并且为了有用，该亚组由少量预测性生物标志物确定。对于随机临床试验的数据，我们建议一个统一的建模方法，并将研究使用单指数模型与变量选择和多元偏最小二乘法，以帮助在亚组识别。亚组识别后的推断是具有挑战性的，我们提出了一个创新的方案来模拟适当的零分布下的数据。 本提案中的所有3个目标都涉及转化肿瘤学研究中的基本和重要问题。这些目标的成功完成将对理解和利用生物标志物以及开发可更广泛适用于其他领域的统计方法产生影响。 公共卫生相关性：生物标志物被认为是癌症预防、检测、治疗和监测方面预期改进的核心组成部分。关于生物标志物的关键问题是它们何时以及是否有效使用，如何以有效和有效的方式利用它们，以及如果使用它们，那么获得的结论的可信度如何。该建议涉及开发用于评价生物标志物数据的适当和有效的统计方法。