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:
    8181612
  • 负责人:
  • 金额:
    $ 16.15万
  • 依托单位:
  • 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 2015-06-30
  • 项目状态:
    已结题

项目摘要

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. PUBLIC HEALTH RELEVANCE: The research proposal addresses the pressing need for advanced statistical tools that meet challenges in current development of prediction models for disease risk and treatment benefit. By providing statistical tools that enable clinical investigators to effectively develop personalized disease management strategies, this proposal will join prior and ongoing research activities towards the goal of finding efficient and cost effective personalized medicine.
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。对于早期发病风险较高的患者,可以给予更积极的干预,而对一种治疗不太可能起作用的患者,应考虑其他选择。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和治疗结果预测的潜在工具,并可能导致个性化,量身定制的医疗。DNA测序和微阵列等新技术正在生成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,本提案旨在开发统计方法,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。具体而言,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,开发准确的风险预测模型。我们还将提供非参数程序来评估所得模型的预测性能。在目标2中,我们提出了使用两阶段研究的绝对风险和新标记物预测性能的推理程序。在目标3中,我们开发了系统的程序,用于识别可能或可能不会从患者水平基线标记信息的新治疗中受益的患者亚组。在目标4中,我们专注于高维回归,并开发正则化重采样方法,以构建回归系数的置信区间和假设检验程序,以及估计模型的预测性能。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使用护士健康研究(NHS)的女性患类风湿关节炎的个体风险;(ii)使用NHS和卫生专业人员随访研究的糖尿病患者的心血管疾病;㈢利用大规模免疫遗传学研究确定艾滋病毒感染者的艾滋病事件;(iv)冠心病或中风(使用妇女健康倡议(WHI)研究)。我们还计划开发算法,利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。确定的病例将用于相应疾病的后续遗传病例对照研究。这种算法将使电子病历临床数据直接用于发现研究。此外,我们将通过随机ACTG临床试验为艾滋病毒感染者制定治疗选择策略,并通过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、可改变的风险因素以及生物标记纳入风险模型可能会改善对临床结果的预测,并最终导致个性化医疗。

项目成果

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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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TIANXI CAI其他文献

TIANXI CAI的其他文献

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{{ truncateString('TIANXI CAI', 18)}}的其他基金

Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies
通过机器学习连接临床试验和真实世界数据,以推进类风湿性关节炎的治疗策略
  • 批准号:
    10652251
  • 财政年份:
    2022
  • 资助金额:
    $ 16.15万
  • 项目类别:
Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies
通过机器学习连接临床试验和真实世界数据,以推进类风湿性关节炎的治疗策略
  • 批准号:
    10339668
  • 财政年份:
    2022
  • 资助金额:
    $ 16.15万
  • 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
  • 批准号:
    10453558
  • 财政年份:
    2021
  • 资助金额:
    $ 16.15万
  • 项目类别:
Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis
研究特殊治疗无反应者和遗传学以预测类风湿关节炎的治疗反应
  • 批准号:
    10430273
  • 财政年份:
    2021
  • 资助金额:
    $ 16.15万
  • 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
  • 批准号:
    10185327
  • 财政年份:
    2021
  • 资助金额:
    $ 16.15万
  • 项目类别:
Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis
研究特殊治疗无反应者和遗传学以预测类风湿关节炎的治疗反应
  • 批准号:
    10301407
  • 财政年份:
    2021
  • 资助金额:
    $ 16.15万
  • 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
  • 批准号:
    10617781
  • 财政年份:
    2021
  • 资助金额:
    $ 16.15万
  • 项目类别:
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:
    7356026
  • 财政年份:
    2007
  • 资助金额:
    $ 16.15万
  • 项目类别:
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
  • 财政年份:
    2007
  • 资助金额:
    $ 16.15万
  • 项目类别:
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:
    8501533
  • 财政年份:
    2007
  • 资助金额:
    $ 16.15万
  • 项目类别:

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