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（由申请方提供）：终末期肾病与死亡率加速相关，心血管（CV）疾病是死亡的主要原因。与更有效地护理透析患者相关的是表征结果轨迹如何随时间演变并识别其相关的风险因素。从透析开始，阐明患者水平风险因素（如感染）和机构水平特征（如机构的患者护理人员组成）随时间对患者CV结局的时变影响非常重要。我们的长期目标是为识别可修改的 患者水平和设施水平的风险因素和透析护理提供者的质量改进方法。为了实现这一目标，我们将开发一个通用框架，以估计和推断患者结局的多级时间动态建模，该模型适应多级数据结构（例如，嵌套在透析设施或护理提供者内的患者和嵌套在患者内的随时间的观察）。我们提出的透析患者CV事件和感染风险因素的时间动态效应的新模型对于设计有效的疾病管理和预防方法非常重要，因为它允许识别风险增加的特定时间段。此外，所提出的框架与设施级决策相关，包括预测透析设施患者护理策略的变化是否会随着时间的推移改善患者结局，以及时间动态性能评估。创新到目前为止，还不存在一个可行的框架，估计和双推理（患者和设施级），在多层次的变系数模型（MVCM），可容纳多层次的纵向数据结构。我们的工作将是第一个同时研究MVCM中患者和设施级推理的工作，并灵活地模拟跨越一系列模型的设施级效应，包括设施（i） 固定效应，（ii）常数随机效应，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，以检查从透析开始的连续透析设施性能评估，并允许识别目标患者结局改善的特定时间段。目标。拟议的框架将通过以下具体目标实现：（受试者水平推断）开发和应用多水平纵向反应（结果）的MVCM，具有一般受试者水平协变量和设施水平效应的灵活建模; 2）（设施级推理）发展和应用MVCM进行设施级推理; 3）（MVCM性能特征）描述MVCM的操作特征，包括跨信息稀疏水平和推理目标的相对效率和对建模假设的敏感性。