Post-selection inference and trajectory analysis

选择后推理和轨迹分析

• 批准号: 9316655
• 负责人: IAN WRAY MCKEAGUE
• 金额: $ 20万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9316655
• 关键词: Address; Adult; Area; Autistic Disorder; Bipolar Disorder; Collaborations; Data; Disease; Drug resistance; Gene Expression; Genetic study; Genomics; Goals; Grant; Growth; HIV Infections; HIV vaccine; Health; Hybrids; Knowledge; Life course epidemiology; Linear Regressions; Malaria; Maternal and Child Health; Measurement; Medical; Methods; Mutation; Neuropsychology; Outcome; Parasites; Pharmaceutical Preparations; Policies; Pregnancy; Principal Investigator; Procedures; Process; Psychotic Disorders; Public Health; Public Health Applications Research; Research Personnel; Resistance; Risk; Sampling Biases; Schizophrenia; Shapes; Southeastern Asia; Statistical Methods; Survival Analysis; Target Populations; Testing; Time; Treatment Effectiveness; Weight Gain; Work; base; biosignature; design; follow-up; genomic epidemiology; hazard; high dimensionality; improved; infancy; killings; novel; personalized medicine; precision medicine; prenatal; public health relevance; response; screening; statistics; survival outcome

 DESCRIPTION (provided by applicant): This is a project to develop new methods of post-selection inference and trajectory analysis directed towards important public health applications in genomics and epidemiology. The broad objective of the project is to provide new methods of post-selection inference for detecting the presence of significant predictors in high- dimensional screening. These methods will be developed with a view to applications in several areas: drug resistance studies, personalized medicine, growth trajectories, and survival outcomes. Screening large numbers of predictors and assessing their utility in treatment decisions is a challenging problem. Among other things, the project will provide a more powerful alternative to the popular (yet conservative) Bonferroni method of controlling familywise error rates that are a crucial concern for these applications. Work on this topic was initiated in the principal investigator's current R01 grant, but was confined to linear regression settings. The significanc of the new application is that it will greatly expand the scope of these methods to allow for their much broader application. High-dimensional screening is especially relevant for extracting predictive features of growth trajectories. In addition, the project will develop new screening tests specifically for the purpose of comparing survival functions. This will be done in terms of nonparametric tests for stochastic ordering and hazard rate ordering under various censoring and biased sampling scenarios. An empirical likelihood approach (more powerful than the Wald approach) will be used. Post- selection inference issues to be addressed in this setting involve devising a way to calibrate maximally selected empirical likelihood-based test statistics over the follow-up period, and in screening for the presence of significant orderings among multiple groups of subjects. A further objective is to develop new methods for reconstructing growth trajectories from sparse temporal data for use as predictors of health outcomes. This work was also initiated in the principal investigator's current R01 grant, and recently used by the principa investigator to study of the association between autism and dynamical features of growth during early infancy. The renewal will focus on improving these trajectory analysis methods by adjusting for measurement error and prior knowledge about the shape and boundedness of growth trajectories, with a view to applications in two new areas: 1) biosignatures in Finnish prenatal studies of schizophrenia, bipolar disorder and related psychotic disorders, and 2) pregnancy weight gain and long term maternal and child health outcomes.

 描述（由应用程序提供）：这是一个项目，旨在开发针对基因组学和流行病学重要公共卫生应用的新方法推理和轨迹分析。该项目的广泛目标是提供新的选择后推断方法，以检测高维预测因子的存在 筛选。将开发这些方法，以考虑在几个领域的应用：耐药性研究，个性化医学，生长轨迹和生存结果。筛选大量预测因素并评估其在治疗决策中的效用是一个挑战问题。除其他事项外，该项目将提供更强大的替代方案，用于控制家庭错误率的流行（但保守）的Bonferroni方法，这是这些应用的关键问题。有关该主题的工作是在首席研究员当前的R01赠款中启动的，但已确定为线性回归设置。新应用程序的重要性是它将大大扩展这些方法的范围，以允许其 更广泛的应用。高维筛选与提取生长轨迹的预测特征特别相关。此外，该项目将开发特定于比较生存功能的新筛选测试。这将在各种审查和偏见的采样场景下的随机排序和危害率排序的非参数测试方面进行。将使用一种经验可能的方法（比Wald方法更强大）。在这种情况下要解决的选择后推断问题涉及一种方法，以在随访期间校准基于经验可能性的测试统计数据，并筛选在多个受试者组之间存在显着订购的方法。一个进一步的目标是开发从稀疏临时数据中重建生长轨迹的新方法，以用作健康结果的预测指标。这项工作还在主要研究者目前的R01赠款中发起，最近由主要研究者使用，研究了婴儿早期生长期的自闭症与动态特征之间的关联。更新将重点放在改善这些轨迹分析方法上，通过调整有关生长轨迹的形状和界限的测量误差和先验知识，以期在两个新领域中的应用：1）芬兰精神分裂症，双极障碍，双极障碍，双极障碍，相关的精神病患者的生物签名，以及长期的孕妇体重和儿童的长期儿童。