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。尽管在一般人群中，急性感染与心肌梗死和中风的风险增加有关，但在透析人群中，感染在多大程度上是心血管事件风险增加的一个促进因素，这在很大程度上是未知的。透析人群研究数据的最大来源是美国肾数据系统数据库，它包含了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和各种危险因素，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推移的趋势，特别是随着透析时间（年）和年龄的变化。确定感染与心血管事件发生之间的年龄和时间依赖关系，并根据预测因子（例如，从前一个月到三个月）获得预测的特定受试者心血管事件的风险轨迹（概率），这是在美国透析人群中制定有针对性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/具有挑战性的结构的方法，其特征是极端（超）稀疏性、不同步测量和不精确/测量误差。这是由于从病人住院记录中收集的数据造成的，而这些数据极不规律和罕见。此外，纵向临床炎症标志物数据（可用于USRDS队列的一个子集）与结果在不同步的时间点，可能受到测量误差的污染。目前还没有对纵向二元结果（如心血管事件的发生）进行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)易出错的纵向数据。目标。拟议的研究将填补这一空白，通过使用功能数据分析（FDA）为这些新兴挑战下的纵向数据开发新的广义半参数回归模型（GSRMs）估计和推理程序。这将通过以下具体目标来实现：1)针对高度不规则、不频繁、不同步和受噪声污染的纵向数据，为gsrm的估计和推理建立统一的功能分析框架，包括广义和广义偏线性变系数模型；2)开发预测受试者特定反应轨迹的方法；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心血管感染风险的纵向动态。

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. PUBLIC HEALTH RELEVANCE: The public health burden directly related to infection and cardiovascular disease in the dialysis population is substantial. The proposal involves developing the necessary estimation and inference framework to use the United States Renal Data System database in modeling age- and time-varying dynamics of the association between cardiovascular events and various contributing risk factors including infection. Understanding this cardiovascular-infection risk dynamics in patients over time is important to the development of targeted intervention strategies in the US dialysis population.