Method Development for Survival Dynamic Regression in Chronic Disease Research

项目摘要 在慢性病研究中，了解和解释由遗传、 环境和生活方式因素对成功的疾病管理变得越来越重要。 动态回归，如最近的工作所表明的，包括我们的工作，是一种强大的技术来表征和 找出解释疾病进展的个体变异性的不同关联。总体目标是- 这笔赠款的目的是推进动态回归方法，以更好地满足 通过提高处理纵向/生存的能力来揭示疾病机制的异质性 各种复杂形式的结果和协变量(例如，时变、高维、受约束的)。 这个应用程序的动机是我们正在进行的婴儿喂养权利方面的合作。从一开始 (一)学习。在为患有囊性纤维化(CF)的婴儿确定最佳护理的超额目标下， First系统地捕获了完整的进食史和纵向收集的生物标志物的数据 (例如，血脂和粪便微生物区系)，并在整个过程中获得营养状况和肺部疾病 婴儿期。凭借丰富的数据收集，First提供了一个前所未有的机会来开发新的明智的 早期CF型(如肺型、生长型)的量子fi阳离子及其与观察到的动态关系 因素(例如，基因、环境因素)；评估CF婴儿的母乳/配方奶喂养方案；fiII 在生物标志物对生长的影响及其与摄食的关系方面的信息差距。 这笔赠款的SPECIfic目的是开发创新和有效的动态回归工具 实现这些有影响力的科学fic目标：(1)我们将调查一个合理的建模视角，该视角关注 论主体层面的潜在特征(下称潜在个体风险特征)作为实质性特征 疾病风险/状态的fl检查(例如长度增长率)。我们将开发正式的动态回归方法 用于描述LiRF中的异质性，这在文献中是不存在的(Aim1)。(二)发展 一种创新的生存动态回归策略，能够全面评估总体 与时间相关的暴露(如喂食史)对生存结果(如至肺的时间)的影响 加重)。目前的方法通常描述随时间变化的协变量的影响 因此，评估不同的喂养方案的效用有限(AIM2)。(三)发展新的 动态回归方法综合考虑了重要的数据挑战/特性(例如 高维、约束、纵向结果、依赖时间的协变量)以进行适当评估 婴儿期生物标志物的作用机制(Aim3)。(4)拟议的统计方法将 将首先应用，并将开发用户友好的软件(目标4-5)。虽然特殊的fi是有动机的 通过对慢性疾病的研究，建议的方法普遍适用于许多其他慢性疾病。

Project Summary In chronic diseases research, understanding and accounting for individual differences caused by genetic, environmental, and lifestyle factors have become increasingly important for successful disease management. Dynamic regression, as shown by recent work including ours, is a powerful technique to characterize and identify inhomogeneous associations that explain individual variability of disease progression. The overall ob- jective of this grant is to advance dynamic regression methodology to better meet the critical need of uncovering disease mechanism heterogeneity with improved capacity to handle longitudinal/survival outcomes and covariates in various complex forms (e.g. time-varying, high-dimensional, constrained). This application is motivated by our ongoing collaborations on Feeding Infants Right.. from the STart (FIRST) study. Under the overreaching goal to identify optimal care for infants with Cystic Fibrosis (CF), FIRST has systematically captured data on complete feeding history and longitudinally collected biomarkers (e.g. blood lipids and fecal microbiota) and accessed nutritional status and pulmonary disease throughout infancy. With the rich data collection, FIRST provides an unprecedented opportunity to exploit new sensible quantifications of early CF phenotype (e.g. pulmonary, growth) and their dynamic associations with observed factors (e.g. genotype, environmental factors); to assess breast/formula feeding schemes for CF infants; to fill in the information gap on the influence of biomarkers on growth and their relationships to feeding. The specific aims of this grant are to develop innovative and effective dynamic regression tools that can help achieve these impactful scientific goals: (1) We will investigate a sensible modeling perspective that focuses on subject-level latent characteristics (called latent individual risk feature (LIRF) hereafter) as a substantive reflection of disease risk/status (e.g. length growth rate ). We will develop formal dynamic regression methods for delineating the heterogeneity in LIRF, which are not available in literature (Aim1). (2) We will develop an innovative survival dynamic regression strategy that enables a comprehensive assessment of the overall impact of time-dependent exposures (e.g. feeding history) on survival outcomes (e.g. time to pulmonary exacerbation). Current methods usually describe the effects of time-dependent covariates progressively over time and thus have limited utility for evaluating different feeding schemes (Aim2). (3) We will develop new dynamic regression approaches that give an integrative account of important data challenges/features (e.g. high-dimensionality, constraints, longitudinal outcomes, time-dependent covariates) for properly assessing the mechanisms/roles of biomarkers during CF infancy (Aim3). (4) The proposed statistical methods will be applied to FIRST and user-friendly software will be developed (Aims 4-5). Although specifically motivated by CF studies, the proposed methodologies are generally applicable to many other chronic diseases.