Robust Approaches to the Development and Evaluation of Prognostic Classifiers

描述(由申请人提供)：可靠和准确的预后是成功的疾病管理和治疗选择的基础。可以对早期发病风险较高的患者进行更积极的干预，而对一种治疗不太可能有反应的患者应考虑替代方案。随着技术的快速进步，广泛的生物和基因组标记已经成为改善疾病预测和治疗结果的潜在工具，并可能导致个性化、定制的药物。DNA测序和微阵列等新技术正在产生维度和复杂性呈指数级增长的详细数据。这些数据为准确预测临床结果提供了前所未有的机遇和巨大的挑战。为了充分利用这些数据，这项建议旨在开发统计方法，以有效地构建和评估疾病风险评估和治疗选择的预后工具。具体地说，在目标1中，我们将通过核机器回归框架结合复杂的交互效应来开发准确的风险预测模型。我们还将提供用于评估结果模型的预测性能的非参数程序。在目标2中，我们通过两个阶段的研究提出了绝对风险和新标记物预测性能的推断程序。在目标3中，我们开发了系统的程序，利用患者水平的基线标记物信息来识别可能受益于或可能不受益于新治疗的患者亚组。在目标4中，我们专注于高维回归，并发展了正则化重采样方法来构造回归系数和估计模型的预测性能的置信度区间和假设检验程序。为了增加我们研究的实际影响，除了创建供公众使用的软件外，我们还将应用拟议的程序来预测以下个人风险：(I)使用护士健康研究(NHS)预测女性中的类风湿性关节炎；(Ii)使用NHS和健康专业人员后续研究预测糖尿病患者中的心血管疾病；(Iii)使用大型免疫遗传学研究确定艾滋病毒感染患者中的艾滋病事件；以及(Iv)使用妇女健康倡议(WHI)研究预测冠心病或中风。我们还计划开发算法，使用波士顿两家大型医院的电子病历(EMR)数据来识别各种自身免疫性疾病的病例。已确定的病例将用于相应疾病的后续遗传病例对照研究。这种算法将使EMR临床数据能够直接用于发现研究。此外，我们将利用随机ACTG临床试验为HIV感染患者制定治疗选择策略，并利用WHI临床试验为预防心血管疾病的饮食干预制定策略。将遗传特征、可修改的风险因素以及生物标记纳入风险模型可能会改善临床结果的预测，并最终导致个性化药物的出现。

DESCRIPTION (provided by applicant): A reliable and precise prognosis is fundamental for successful disease management and treatment selection. More aggressive intervention can be given to patients who are at high risk of early disease onset, while patients who are unlikely to respond to one treatment should be considered for alternative options. With the rapid advancement of technology, a wide range of biological and genomic markers have emerged as potential tools for improving the prediction of disease and treatment outcomes, and may lead to personalized, tailored medicine. New technologies such as DNA sequencing and microarrays are generating detailed data with exponentially increasing dimensionality and complexity. These data presents unprecedented opportunities and great challenges for making accurate prediction of clinical outcomes. To take full advantage of such data, this proposal aims to develop statistical approaches to efficiently construct and evaluate prognostic tools for disease risk assessment and treatment selection. Specifically, in Aim 1, we will develop accurate risk prediction models by incorporating complex interactive effects via a kernel machine regression framework. We will also provide non-parametric procedures for assessing the predictive performance of the resulting models. In Aim 2, we propose inference procedures for absolute risks and prediction performance of new markers using two-phase studies. In Aim 3, we develop systematic procedures for identifying subgroups of patients who may or may not benefit from a new treatment using patient level baseline marker information. In Aim 4, we focus on high dimensional regression and develop regularized resampling methods to construct confidence intervals and hypothesis testing procedures for regression coefficients and the prediction performance of estimated models. To increase the practical impact of our research, in addition to creating software for public use, we will apply the proposed procedures to predict individual risk of developing (i) rheumatoid arthritis among women using the Nurse's Health Study (NHS); (ii) CVD among diabetic patients using the NHS and the Health Professional Follow-up Study; (iii) AIDS defining events among HIV infected patients using a large immunogenetic study; and (iv) CHD or stroke using the Women's Health Initiative (WHI) study. We also plan to develop algorithms to identify cases of various autoimmune diseases using electronic medical record (EMR) data from two large hospitals in Boston. The identified cases will be used for subsequent genetic case-control studies of the corresponding diseases. Such algorithms will enable the use of EMR clinical data directly for discovery research. In addition, we will develop treatment selection strategies for HIV infected patients using randomized ACTG clinical trials and for dietary intervention in preventing CVD using WHI clinical trials. Incorporating genetic profile, modifiable risk factors, along with biologic markers into risk models is likely to improve the prediction of clinical outcomes and ultimately lead to personalized medicine.