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
• 负责人: TIANXI CAI
• 金额: $ 15.58万
• 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 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8501533
• 关键词: AIDS clinical trial group; Acquired Immunodeficiency Syndrome; Address; Algorithms; Autoimmune Diseases; Bioconductor; Biological; Biological Markers; Boston; Breast Cancer Risk Assessment Tool; Cardiovascular Diseases; Case-Control Studies; Clinical; Clinical Data; Clinical Investigator; Clinical Research; Clinical Trials; Communities; Complex; Computer software; Computerized Medical Record; Confidence Intervals; DNA Microarray Chip; DNA Sequence; Data; Development; Dietary Intervention; Disease; Disease Management; Disease Outcome; Environment; Evaluation; Event; Genes; Genetic; Genetic Markers; Genomics; Goals; HIV; Health Professional; Hemorrhage; Hospitals; Immunogenetics; Individual; Internet; Intervention; Lead; Manuscripts; Measures; Medicine; Methods; Modeling; Nurses' Health Study; Onset of illness; Outcome; Pathway interactions; Patients; Performance; Phase; Principal Component Analysis; Procedures; Public Health; Randomized; Research; Research Activity; Research Design; Research Personnel; Research Proposals; Rest; Rheumatoid Arthritis; Risk; Risk Assessment; Risk Factors; Science; Selection for Treatments; Series; Software Tools; Source Code; Stroke; Subgroup; Technology; Testing; Treatment outcome; Woman; Women' s Health; Work; base; cardiovascular disorder risk; computer code; cost; cost effective; design; diabetic patient; disorder risk; follow-up; genetic profiling; high risk; high throughput technology; improved; meetings; model development; modifiable risk; new technology; novel; novel marker; open source; outcome forecast; prevent; prognostic; tool; treatment effect; web site

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.

描述（由申请人提供）：可靠而精确的预后对于成功的疾病管理和治疗选择是基础。可以对患有早期疾病的高风险的患者进行更积极的干预，而不太可能反应一种治疗的患者应考虑替代选择。随着技术的快速发展，已经成为改善疾病和治疗结果预测的潜在工具，并可能导致个性化的，量身定制的医学。 DNA测序和微阵列之类的新技术正在生成具有指数增长的维度和复杂性的详细数据。这些数据提出了前所未有的机会，以及对临床结果进行准确预测的巨大挑战。为了充分利用此类数据，该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AIM 1中，我们将通过内核机器回归框架合并复杂的交互式效果来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AIM 2中，我们提出了使用两阶段研究的绝对风险和预测新标记性能的推理程序。在AIM 3中，我们开发了系统的程序来识别使用患者水平基线标记信息从新治疗中受益的患者的亚组。在AIM 4中，我们专注于高维回归，并开发正规化的重采样方法，以构建回归系数的置信区间和假设测试程序，以及估计模型的预测性能。为了增加我们的研究的实际影响，除了创建用于公共使用的软件外，我们还将采用拟议的程序来预测使用护士健康研究（NHS）女性中（i）妇女中（i）类风湿关节炎的风险； （ii）使用NHS和卫生专业后续研究的糖尿病患者中的CVD； （iii）使用大型免疫遗传研究来定义艾滋病毒感染患者之间的事件； （iv）使用妇女健康计划（WHI）研究的CHD或中风。我们还计划开发算法，以使用来自波士顿的两家大型医院的电子病历（EMR）数据来鉴定各种自身免疫性疾病的病例。确定的病例将用于随后对相应疾病的遗传病例对照研究。这种算法将使EMR临床数据直接用于发现研究。此外，我们还将使用随机ACTG临床试验制定HIV感染患者的治疗选择策略，并针对使用WHI临床试验的CVD进行饮食干预。将遗传概况，可修改的危险因素以及生物标志物纳入风险模型可能会改善临床结果的预测，并最终导致个性化医学。