Effective semiparametric models for ultra-sparse, unsynchronized, imprecise data

描述（由申请人提供）：感染和心血管疾病是透析人群死亡的两个主要来源。尽管急性感染与一般人群中心肌梗塞和中风的风险增加有关，但感染在多大程度上是透析人群中纵向心血管事件风险增加的影响因素尚不清楚。透析人群研究数据的最大来源是美国肾脏数据系统数据库，其中包含几乎所有维持性透析患者的住院记录。我们的长期目标是研究心血管事件与各种危险因素（特别是感染）之间的动态关联。为了实现这一目标，我们将开发广义半参数回归模型来研究随时间变化的趋势，特别是透析随时间（年）变化的趋势和随年龄变化的趋势。确定感染与心血管事件发生之间的年龄和时间依赖性关联，并根据预测因素获得心血管事件的预测受试者特定风险轨迹（概率），例如，从前一到三个月（即时间滞后预测），是在美国透析人群中制定有针对性的干预策略的关键步骤。创新。实现这一目标的主要挑战是缺乏能够处理可用于分析的纵向数据的极端/挑战性结构的方法，其特征是极端（超）稀疏、不同步测量和不精确/测量误差。这是从患者住院记录中收集的数据得出的结果，这些数据非常不规则且罕见。此外，纵向临床炎症标志物数据（可用于 USRDS 队列的一个子集）与结果处于不同步的时间点，可能受到测量误差的污染。目前，尚无用于纵向二元结果（例如，心血管事件的发生）的广义半参数回归建模或计数/率结果建模的方法可以处理1）不规则、2）不频繁、3）不同步和4）容易出错的纵向数据。目标。拟议的研究将通过使用功能数据分析（FDA）在这些新兴挑战下为纵向数据的广义半参数回归模型（GSRM）开发新的估计和推理程序来填补这一空白。这将通过以下具体目标来实现： 1）针对高度不规则、罕见、不同步和噪声污染的纵向数据，开发一个统一的 GSRM 估计和推理功能分析框架，包括广义和广义部分线性变系数模型； 2）开发预测特定受试者反应轨迹的方法； 3) 描述我们提出的 FDA 方法的效率。此外，这些方法将首次用于确定透析人群的心血管感染风险纵向动态。

DESCRIPTION (provided by applicant): Infection and cardiovascular disease are two main sources of mortality in the dialysis population. Even though acute infections have been associated with an increased risk of myocardial infarction and stroke in the general population, the extent to which infection is a contributing factor to increased risk of cardiovascular events longitudinally in the dialysis population is largely unknown. The largest source of research data for the dialysis population is the United States Renal Data System database, which contains hospitalization records of nearly all patients on maintenance dialysis. Our long-term goal is to study the dynamic association of cardiovascular events and various contributing risk factors, particularly infection. Towards this goal, we will develop generalized semiparametric regression models to study trends over time generally, over time (years) on dialysis and over age, specifically. Determining the age- and time-dependent association between infection and the occurrence of cardiovascular events and obtaining the predicted subject- specific risk trajectory (probability) of cardiovascular events based on predictors, for instance, from the previous one to three months (i.e., time-lagged prediction) are critical steps towards the development of targeted intervention strategies in the US dialysis population. Innovation. The main challenge towards this goal is the lack of methods able to handle the extreme/ challenging structure of the longitudinal data available for analysis, characterized by extreme- (ultra-) sparsity, unsynchronized measurements, and imprecision/measurement error. This results from data collected on patient hospitalization records, which is extremely irregular and infrequent. In addition, longitudinal clinical inflammatory markers data (available for a subset of the USRDS cohort) are at unsynchronized time points with the outcome, possibly contaminated with measurement error. Currently there are no existing methods for generalized semiparametric regression modeling of longitudinal binary outcome (e.g., occurrence of cardiovascular events) or modeling of count/rate outcome that can handle 1) irregular, 2) infrequent, 3) unsynchronized and 4) error-prone longitudinal data. Aims. The proposed research will fill this gap, by developing new estimation & inference procedures for generalized semiparametric regression models (GSRMs) for longitudinal data under these emerging challenges using functional data analysis (FDA). This will be achieved through the following specific aims: 1) Develop a unified functional analysis framework for estimation and inference for GSRMs, including generalized and generalized partial linear varying coefficient models, for highly irregular, infrequent, unsynchronized and noise-contaminated longitudinal data; 2) Develop methods to predict subject-specific response trajectories; 3) Characterize the efficiency of our proposed FDA approach. Furthermore, these methods will be used to determine, for the first time, the cardiovascular-infection risk longitudinal dynamics in the dialysis population.