Method Development for Survival Dynamic Regression in Chronic Disease Research

项目摘要 在慢性病研究中，理解和解释由遗传， 环境和生活方式因素对于成功的疾病管理变得越来越重要。 动态回归，如最近的工作所示，包括我们的，是一种强大的技术来表征和 识别解释疾病进展个体差异的不均匀关联。总体上，Ob- 该补助金的目的是推进动态回归方法，以更好地满足 揭示疾病机制异质性，提高处理纵向/生存期的能力 各种复杂形式的结果和协变量（例如，时变、高维、约束）。 这个应用程序的动机是我们正在进行的合作喂养婴儿的权利。从一开始 （一）研究。在确定囊性纤维化（CF）婴儿的最佳护理的过度目标下， FIRST系统地收集了完整的喂养史数据和纵向收集的生物标志物 (e.g.血脂和粪便微生物群），并在整个过程中评估营养状况和肺部疾病 婴儿期。随着丰富的数据收集，FIRST提供了一个前所未有的机会，以开发新的明智的 早期CF表型（例如，肺部，生长）的定量及其与观察到的 因素（例如基因型、环境因素）;评估CF婴儿的母乳/配方奶粉喂养计划; 生物标志物对生长的影响及其与喂养的关系的信息缺口。 该补助金的具体目标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目标：（1）我们将研究一个合理的建模视角， 受试者层面的潜在特征（以下称为潜在个体风险特征（LIRF））作为实质性的 疾病风险/状态的反映（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，这在文献中不可用（Aim 1）。(2)我们将开发 一种创新的生存动态回归策略，能够全面评估 时间依赖性暴露（如喂养史）对生存结局（如至肺动脉栓塞时间）的影响 加重）。目前的方法通常描述时间依赖性协变量的影响， 时间，因此对于评估不同的喂养方案（Aim 2）的效用有限。(3)发展新型 综合考虑重要数据挑战/特征的动态回归方法（例如， 高维度、限制因素、纵向结果、时间依赖性协变量）进行适当评估 CF婴儿期生物标志物的机制/作用（Aim 3）。(4)拟议的统计方法将 并将开发方便用户的软件（目标4-5）。虽然有特殊动机， 通过CF研究，所提出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.