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

基本信息

批准号：
7356026
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
项目类别：
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

项目摘要

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的预测在医疗保健中至关重要。与预后群体不正确分配有关的潜在临床和财务后果表示对可靠的预后指数的需求以及对其准确性的严格评估。对于复杂的疾病，任何单个标记通常都不足以进行精确预测。随着新的预后标记的可用性大大提高，现在可以通过结合几种标记的信息来提高预后准确性。这引起了对来自多种来源的信息最佳用途的最佳用法的需求，以改善疾病管理。我们的建议旨在制定解决这一需求的程序。在旨在开发预后分类器的研究中，通常在基线时测量标记物，并且随着时间的流逝，患者以临床状况的发生。由于发生这种疾病的风险可能会随着时间而变化，因此在开发预后分类器时必须纳入时间域。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。当前用于分析事件时间数据的统计文献主要关注基于模型的方法及其有效性依赖于模型假设。这种假设可能无法在实践中成立，这可能导致偏见或无效的预测。在此提案中，我们考虑了预后分类器的开发和评估的强大方法。我们将重点关注以下三个目标。在AIM 1中，我们将开发出可靠的方法来基于多个标记来构建最佳复合分数。在AIM 2中，我们将评估和比较估计预后分数的预后潜力，并制定分配预后群体的最佳决策规则。在AIM 3中，我们将提供程序来确定将从初步评估中受益于潜在昂贵或侵入性预后评估的受试者。该项目可以访问各种各样的实际数据集，以指导方法论研究。例子包括1）来自诊断患有肺栓塞的患者的研究数据； 2）来自心血管健康研究的数据； 3）来自乳腺癌研究的基因表达数据； 4）来自AIDS临床试验的数据。我们的目标将需要发展大型样本分布理论，小样本模拟研究以及对真实数据的应用。实施分析的软件将使用标准的统计软件包，例如SPLUS或SAS，并将充分记录。