Robust Approaches to the Development and Evaluation of Prognostic Classifiers

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至关重要。与不正确的预后分组相关的潜在临床和经济后果表明，需要可靠的预后指数并对其准确性进行严格评估。对于复杂的疾病，任何单一的标记物通常都不足以准确预测。随着新的预后标记物的可获得性显著增加，现在可以通过结合几个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程序来满足这一需求。在旨在开发预后分类器的研究中，标记物通常在基线上进行测量，并随着时间的推移对患者进行跟踪，以确定临床情况的发生。由于疾病发生的风险可能会随着时间的推移而变化，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。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。这样的假设在实践中可能不成立，这可能导致预测有偏差或无效。在这项建议中，我们考虑了稳健的方法来开发和评估预后分类器。我们将重点抓好以下三个方面的工作。在目标1中，我们将开发基于多个标记构建最优综合分数的稳健方法。在目标2中，我们将评估和比较估计的预后评分的预后潜力，并制定最优决策规则来分配预后组。在目标3中，我们将提供程序，以确定哪些受试者将受益于给出初步评估的潜在昂贵或侵入性预后评估。该项目可以获得指导方法论研究的各种真实数据集。例如，1)诊断为肺栓塞的患者的研究数据；2)心血管健康研究的数据；3)乳腺癌研究的基因表达数据；4)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、小样本模拟研究和对真实数据的应用。实施分析的软件将使用标准统计软件包，如sPlus或SAS，并将完全记录在案。

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.