Dynamic prediction of type 1 diabetes risk and autoantibody status by a joint model of longitudinal and multistate models

通过纵向和多状态模型的联合模型动态预测1型糖尿病风险和自身抗体状态

• 批准号: 10630731
• 负责人: Lu You
• 金额: $ 14.99万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10630731
• 关键词: Algorithms; Autoantibodies; Birth; Characteristics; Child; Clinical; Cohort Studies; Complex; Complications of Diabetes Mellitus; Cost of Illness; Data; Data Set; Development; Diabetes Mellitus; Diabetes autoantibodies; Diagnosis; Diagnostic; Disease; Disease Progression; Early identification; Event; Future; Genetic Predisposition to Disease; Genetic Risk; Goals; High Performance Computing; Immunologics; Individual; Infrastructure; Injections; Insulin; Insulin deficiency; Insulin-Dependent Diabetes Mellitus; Joints; Measurement; Measures; Metabolic; Methodology; Methods; Modeling; Monitor; Natural History; Patients; Pattern; Performance; Physicians; Probability; ROC Curve; Recording of previous events; Research; Risk; Risk Factors; Specificity; Statistical Methods; Statistical Models; Structure; Structure of beta Cell of islet; Technology; Time; Update; chronic autoimmune disease; complex data; diabetes risk; disorder risk; flexibility; hazard; health management; high risk; improved; insulin dependent diabetes mellitus onset; islet cell antibody; novel diagnostics; personalized predictions; prediction algorithm; predictive modeling; prevent; risk stratification; semiparametric; time use

Project Summary/Abstract Type 1 diabetes is a chronic autoimmune disease that features the destruction of pancreatic beta-cells resulting in insulin deficiency and daily insulin injections for survival. Early identification of type 1 diabetes can be achieved by continuously monitoring islet autoantibody status and longitudinal markers that measure the immunological and metabolic functions. The goal of this proposal is to develop a statistical model that can give dynamic predictions about type 1 diabetes risk based on autoantibody status and the historical data of an individual. A longitudinal model for characterizing time-varying risk factors, a multistate model for predicting autoantibody status, and a survival model for predicting disease progression will be combined in a joint model to achieve the goal. The model will be applied to a dataset derived from The Environmental Determinants of Diabetes in the Young (TEDDY) study. It may be challenging to develop a model with such a complex structure. However, the advances in statistical methodology and computational technology have opened up opportunities to resolve the problems. In Aim 1, we will formulate the proposed joint model and apply it to the TEDDY data. Statistical inferences can be made to investigate how the changes in diabetes-related antoantibodies and other longitudinal risk factors are associated with the risk for type 1 diabetes diagnosis. In Aim 2, based on the proposed joint model, a dynamic prediction algorithm will be derived that predicts autoantibody development and the subsequent risk of type 1 diabetes given the historical data of an individual. Lastly, in Aim 3, we will evaluate the accuracy of the proposed dynamic prediction algorithm using a variety of diagnostic measures. We expect that the proposed joint model will demonstrate better performance than the conventional static survival models that use baseline characteristics or last available measurements. The proposed research can answer critical research questions about the natural history of type 1 diabetes and the relationship between longitudinal risk factors.

项目摘要/摘要 1型糖尿病是一种慢性自身免疫性疾病，其特征是破坏胰腺β细胞 导致胰岛素缺乏症和每日胰岛素注射以生存。 1型糖尿病的早期鉴定可以 可以通过连续监测胰岛自动抗体状态和纵向标记来实现 免疫学和代谢功能。该建议的目的是开发一个可以 根据自身抗体状态和一个历史数据，给出有关1型糖尿病风险的动态预测 个人。一个用于表征时变风险因素的纵向模型，这是一种预测的多态模型 自身抗体状态和预测疾病进展的生存模型将合并为联合模型 实现目标。该模型将应用于源自从环境决定因素的数据集 年轻（泰迪）研究中的糖尿病。开发具有如此复杂结构的模型可能会受到挑战。 但是，统计方法论和计算技术的进步已经打开了机会 解决问题。在AIM 1中，我们将制定提出的联合模型并将其应用于泰迪数据。 可以做出统计推断，以研究与糖尿病相关的Anoantibodies和其他的变化 纵向风险因素与1型糖尿病诊断的风险有关。在AIM 2中，基于 提出的联合模型，将得出一种动态预测算法，即预测自身抗体发展 随后的1型糖尿病风险给出了一个人的历史数据。最后，在AIM 3中，我们将 使用各种诊断测量值评估提出的动态预测算法的准确性。 我们预计所提出的联合模型将表现出比常规静态的表现更好的性能 使用基线特征或最后可用测量的生存模型。拟议的研究可以 回答有关1型糖尿病的自然历史的关键研究问题以及 纵向风险因素。