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

针对超稀疏、不同步、不精确数据的有效半参数模型

• 批准号: 8158712
• 负责人: Damla Senturk
• 金额: $ 22.27万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8158712
• 关键词: Acute; Address; Age; Cardiovascular Diseases; Cardiovascular system; Characteristics; Chronic Disease; Clinical; Data; Data Analyses; Databases; Development; Dialysis procedure; Estimation Techniques; Event; General Population; Goals; Hospitalization; Individual; Infection; Information Systems; Intervention; Kidney; Least-Squares Analysis; Longitudinal Studies; Maintenance; Measurement; Methods; Modeling; Myocardial Infarction; Noise; Outcome; Participant; Patient Monitoring; Patients; Pattern; Population; Prevention strategy; Probability; Procedures; Public Health; Records; Relative (related person); Research; Research Design; Risk; Risk Factors; Sampling; Schedule; Source; Stroke; Structure; Techniques; Time; United States; Visit; Work; base; cardiovascular infection; cardiovascular risk factor; cohort; flexibility; follow-up; functional gain; high risk; inflammatory marker; innovation; insight; interest; mortality; population based; response; treatment strategy; trend

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.

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卒中风险增加相关，但在透析人群中，感染在多大程度上是纵向心血管事件风险增加的促成因素，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脏数据系统数据库，其中包含几乎所有维持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系。为了实现这一目标，我们将开发广义半参数回归模型，以研究一般情况下随时间的趋势，特别是随透析时间（年）和年龄的趋势。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，并基于预测因子获得心血管事件的预测受试者特异性风险轨迹（概率），例如，从前一个月到三个月（即，时滞预测）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/具有挑战性的结构的方法，其特征在于极端（超）稀疏性、不同步测量和不精确/测量误差。这是因为收集的病人住院记录数据极不规则，也不常见。此外，纵向临床炎症标志物数据（可用于USRDS队列的一个子集）在与结果不同步的时间点，可能受到测量误差的污染。目前还没有用于纵向二元结果的广义半参数回归建模的现有方法（例如，心血管事件的发生）或可以处理1）不规则、2）不频繁、3）不同步和4）易出错的纵向数据的计数/速率结果的建模。目标。拟议的研究将填补这一空白，通过开发新的估计和推理程序的广义半参数回归模型（GSRM）的纵向数据下，这些新兴的挑战，使用功能数据分析（FDA）。这将通过以下具体目标来实现：1）针对高度不规则、不频繁、不同步和噪声污染的纵向数据，开发用于GSRM估计和推断的统一功能分析框架，包括广义和广义部分线性变系数模型; 2）开发预测受试者特定响应轨迹的方法; 3）表征我们提出的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。 公共卫生相关性：透析人群中与感染和心血管疾病直接相关的公共卫生负担很大。该提案涉及开发必要的估计和推理框架，以使用美国肾脏数据系统数据库对心血管事件与包括感染在内的各种促成风险因素之间的关联的年龄和时间变化动态进行建模。随着时间的推移，了解患者的心血管感染风险动态对于在美国透析人群中制定有针对性的干预策略非常重要。