Post-selection inference and trajectory analysis

 描述(由申请人提供)：这是一个项目，旨在开发新的选择后推断和轨迹分析方法，针对基因组学和流行病学中的重要公共卫生应用。该项目的主要目标是提供新的选择后推理方法，用于检测高维空间中是否存在显著的fi不能预测因素 放映。这些方法的开发将着眼于在几个领域的应用：耐药性研究、个性化药物、生长轨迹和生存结果。筛选大量的预测因子并评估它们在治疗决策中的有效性是一个具有挑战性的问题。在其他方面，该项目将提供一个更强大的替代方法，流行的(但保守的)Bonferroni方法来控制家庭误码率，这是这些应用程序的关键问题。关于这一主题的工作是在首席研究员目前的R01拨款中启动的，但被fi限制为线性回归设置。新应用程序的特点是它将极大地扩展这些方法的范围，以允许它们的 应用范围更广。高维筛选对于提取生长轨迹的预测特征尤其相关。此外，该项目将开发新的筛查测试，特别是fi，以比较生存函数的目的。这将通过在各种审查和有偏抽样情况下的随机排序和危险率排序的非参数检验来完成。将使用经验似然方法(比Wald方法更强大)。在这一背景下要解决的选择后推理问题包括设计一种方法来校准在后续时间内最大限度地选择的基于经验似然的检验统计量，以及在多组受试者中筛选是否存在显著的fiCant排序。另一个目标是开发新的方法，从稀疏的时间数据中重建增长轨迹，用作健康结果的预测指标。这项工作也是在首席研究人员目前的R01拨款中开始的，最近被原理研究人员用来研究自闭症和婴儿早期生长的动态特征之间的联系。更新将侧重于通过调整测量误差和关于生长轨迹形状和有界性的先验知识来改进这些轨迹分析方法，以期在两个新领域中应用：1)芬兰对精神分裂症、双相情感障碍和相关精神障碍的产前研究中的生物签名，以及2)妊娠体重增加和长期母婴健康结果。

 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.