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.

项目总结/文摘