Modeling Time-Dynamic Multilevel Outcomes in Patients on Dialysis

透析患者的时间动态多层次结果建模

• 批准号: 9022362
• 负责人: Damla Senturk
• 金额: $ 33.93万
• 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 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9022362
• 关键词: Address; Age; Calibration; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Caring; Case Mixes; Case-Mix Adjustments; Cause of Death; Characteristics; Chronic Disease; Complex; Data; Decision Making; Dialysis procedure; Disease Management; End stage renal failure; Evaluation; Event; Funding; Goals; Grant; Hospitalization; Hospitals; Incidence; Infection; Information Systems; Kidney; Lead; Linear Models; Literature; Methods; Modeling; National Institute of Diabetes and Digestive and Kidney Diseases; Normalcy; Outcome; Patient Care; Patient risk; Patient-Focused Outcomes; Patients; Performance; Population; Positioning Attribute; Probability; Procedures; Provider; Reference Standards; Risk; Risk Factors; Risk Reduction; Sentinel; Severity of illness; Structure; Time; United States; Work; cardiovascular risk factor; design; disorder prevention; flexibility; improved; innovation; insight; mortality; multilevel analysis; novel; outcome prediction; patient oriented; public health relevance; response; semiparametric; simulation

 DESCRIPTION (provided by applicant): End stage renal disease is associated with accelerated mortality, and cardiovascular (CV) disease is the leading cause of death. Of relevance to more effective care of patients on dialysis is characterizing how outcome trajectories evolve over time and identifying their associated risk factors. Elucidating the time-varying effects of patient-level risk factors, such as infection, and facility-level characteristic, such as facilities' patient care staffing composition, on patients' CV outcome over time, from the start of dialysis is important. Our long-term goal is to provide guidance in identifying modifiable patient-level and facility-level risk factors and approaches to quality improvement of dialysis care providers. Towards this goal, we will develop a general framework to estimation and inference for multilevel time-dynamic modeling of patient outcomes that accommodates multilevel data structures (e.g., patients nested within dialysis facilities or care providers and observations over time nested within patients). Our proposed novel modeling of time- dynamic effects of risk factors of CV events and infection in patients on dialysis, is important for designing effective approaches to disease management and prevention because it allows identification of specific time periods of increased risk. In addition, the proposed framework is o relevant to facility-level decision making, including prediction of whether changes in a dialysis facility's patient care strategy would lead to improved patient outcome over time, as well as time-dynamic performance evaluation. Innovation. To date, there does not exist a feasible framework for estimation and dual inference (patient- and facility-level) in multilevel varying coefficient modeling (MVCM) that accommodates multilevel longitudinal data structures. Our work will be the first to study both patient- and facility-level inference in MVCM simultaneously and to flexibly model facility-level effects that span a spectrum of models, including facility (i) fixed effects, (ii) constant random effects, and (iii) random effects functions of time (random coefficient functions). This will also be the first study to examine continuous dialysis facility performance assessment from initiation of dialysis and allow for identification of specific time periods for targeted patient outcome improvement. Aims. The proposed framework will be achieved through the following specific aims: 1) (Subject-level Inference) To develop and apply MVCM for multilevel longitudinal response (outcome) with general subject- level covariates and flexible modeling of facility-level effects; 2) (Facility-level Inference) To develop and apply MVCM for facility-level inference; 3) (MVCM Performance Characteristics) To characterize the operational characteristics of MVCM, including relative efficiency and sensitivity to modeling assumptions, across information sparsity levels and inferential goals.

 描述（由适用提供）：末期肾脏疾病与加速死亡率有关，心血管（CV）疾病是死亡的主要原因。与透析患者更有效护理的相关性是表征结果轨迹随着时间的流逝而发展并确定其相关风险因素的发展。从透析开始，阐明患者级危险因素（例如感染和设施级特征）对患者的CV结果等设施级别的特征（例如设施的患者护理人员组成）的时间变化的影响很重要。我们的长期目标是为识别可修改的指导提供指导 患者级别和设施级别的风险因素以及透析护理提供者质量改善的方法。为了实现这一目标，我们将开发一个一般框架，以对适合多层次数据结构的患者结局进行多层次时间范围建模的估计和推断（例如，嵌套在透析设施中或CARE提供者中嵌套的患者以及随时间嵌套在患者中的患者）。我们提出的对透析的CV事件风险因素和感染的时间动态影响的新型建模对于设计有效的疾病管理和预防方法非常重要，因为它允许识别出增加风险增加的特定时间段。此外，提出的框架与设施级决策有关，包括预测透析设施的患者护理策略的变化是否会随着时间的流逝而改善患者的结果，以及时间动态性绩效评估。创新。迄今为止，在多层次变化的核心建模（MVCM）中，还没有可行的框架来估算和双重推理（患者和设施级别），该框架可容纳多级纵向数据结构。我们的工作将是第一个简单地研究MVCM中患者和设施级别的推断的人，并灵活地建模设施级别的效果，这些效应涵盖了包括设施（I）在内的各种模型（包括设施） 固定效果，（ii）恒定随机效应和（iii）时间的随机效应函数（随机系数函数）。这也将是第一项研究透析启动透析设施绩效评估的第一项研究，并允许识别针对目标患者结果改善的特定时间段。目标。提出的框架将通过以下特定目的来实现：1）（主题级别的推断）开发和应用MVCM，以通过一般主题级别的协变量和设施级效应的灵活建模来开发和应用MVCM进行多层次纵向响应（结果）； 2）（设施级别的推理）开发和应用MVCM进行设施级别的推断； 3）（MVCM性能特征）表征MVCM的操作特征，包括相对效率和对建模假设的敏感性，跨信息稀疏度和推论目标。