Robust Approaches to the Development and Evaluation of Prognostic Classifiers

预后分类器开发和评估的稳健方法

• 批准号: 7185413
• 负责人: TIANXI CAI
• 金额: $ 12.3万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7185413
• 关键词: Acquired Immunodeficiency Syndrome; Address; Cardiovascular system; Classification; Clinical; Clinical Trials; Complex; Computer software; Condition; Data; Data Set; Development; Diagnosis; Disease; Disease Management; Disease regression; Evaluation; Event; Financial cost; Gene Expression; Health; Healthcare; Invasive; Lead; Literature; Malignant Neoplasms; Measures; Methods; Modeling; Numbers; Patients; Procedures; Prognostic Marker; Pulmonary Embolism; Research; Research Design; Research Personnel; Risk Assessment; SAS; Sampling; Score; Source; Standards of Weights and Measures; Technology; Time; Work; base; disorder risk; improved; indexing; malignant breast neoplasm; outcome forecast; prognostic; response; simulation; theories; tool

DESCRIPTION (provided by applicant): Accurate risk assessment and prediction of treatment responses are essential in health care. The potential clinical and financial consequences associated with incorrect assignment of prognostic groups signify the need for reliable prognostic indices and the rigorous evaluation of their accuracy. For complex diseases, any single marker is often inadequate for precise prediction. With dramatically increased availability of new prognostic markers, it is now possible to improve prognostic accuracy by combining information from several markers. This gives rise to the need for statistical approaches to the optimal usage of information from multiple sources to improve disease management. Our proposal aims to develop procedures to address this need. In studies designed to develop prognostic classifiers, markers are often measured at baseline and patients are followed over time for the occurrence of clinical conditions. Since the risk for the disease occurrence may change over time, the time domain must be incorporated when developing prognostic classifiers. Another challenge that arises is that the event times are not always observable due to censoring. Current statistical literature for analyzing event time data focuses primarily on model based methods and their validity relies on the model assumption. Such assumptions may not hold in practice, which may lead to biased or invalid predictions. In this proposal, we consider robust approaches to the development and evaluation of prognostic classifiers. We will focus on the following three aims. In Aim 1, we will develop robust methods for constructing an optimal composite score based on several markers. In Aim 2, we will evaluate and compare the prognostic potential of estimated prognostic scores and develop optimal decision rules for assigning prognostic groups. In Aim 3, we will provide procedures for identifying subjects who would benefit from a potentially expensive or invasive prognostic evaluation given an initial assessment. This project has access to a wide variety of real datasets which will guide the methodological research. Examples include 1) data from a study of patients diagnosed with pulmonary embolism; 2) data from the Cardiovascular Health Study; 3) gene expression data from a breast cancer study; and 4) data from an AIDS clinical trial. Our aims will require development of large sample distribution theory, small sample simulation studies and application to real data. Software to implement analyses will use standard statistical packages such as Splus or SAS and will be fully documented.

描述（由申请人提供）：准确的风险评估和治疗反应预测在卫生保健中是必不可少的。与不正确的预后组分配相关的潜在临床和财务后果表明需要可靠的预后指标和对其准确性的严格评估。对于复杂的疾病，任何单一的标记往往不足以进行精确的预测。随着新的预后标记物的显著增加，现在可以通过结合几种标记物的信息来提高预后的准确性。这就需要采用统计方法来最佳地利用来自多个来源的信息，以改进疾病管理。我们的建议旨在制定解决这一需求的程序。在旨在发展预后分类器的研究中，通常在基线时测量标记物，并随时间跟踪患者临床状况的发生。由于疾病发生的风险可能随着时间的推移而改变，因此在开发预后分类器时必须纳入时域。出现的另一个挑战是，由于审查，事件时间并不总是可观察到的。目前用于分析事件时间数据的统计文献主要集中在基于模型的方法上，其有效性依赖于模型假设。这些假设在实践中可能不成立，这可能导致有偏见或无效的预测。在本提案中，我们考虑稳健的方法来发展和评估预后分类器。我们将着重实现以下三个目标。在目标1中，我们将开发基于多个标记构建最佳复合分数的鲁棒方法。在目标2中，我们将评估和比较估计预后评分的预后潜力，并制定分配预后组的最佳决策规则。在目标3中，我们将提供确定受试者的程序，这些受试者将从潜在的昂贵或侵入性预后评估中获益。该项目可以访问各种各样的真实数据集，这些数据集将指导方法学研究。例子包括：1)来自诊断为肺栓塞患者的研究数据；2)来自心血管健康研究的数据；3)乳腺癌研究的基因表达数据；4)来自艾滋病临床试验的数据。我们的目标将需要发展大样本分布理论，小样本模拟研究和应用于实际数据。实现分析的软件将使用标准的统计软件包，如Splus或SAS，并将完全文档化。