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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要 1 型糖尿病是一种慢性自身免疫性疾病，其特征是胰腺 β 细胞遭到破坏导致胰岛素缺乏，每天注射胰岛素可以早期识别 1 型糖尿病。通过持续监测胰岛自身抗体状态和测量胰岛自身抗体的纵向标记来实现该提案的目标是开发一种可以实现免疫和代谢功能的统计模型。根据自身抗体状态和历史数据对 1 型糖尿病风险进行动态预测用于表征随时间变化的风险因素的纵向模型，用于预测的多状态模型。自身抗体状态和预测疾病进展的生存模型将合并在一个联合模型中为了实现这一目标，该模型将应用于源自环境决定因素的数据集。年轻糖尿病（TEDDY）研究开发具有如此复杂结构的模型可能具有挑战性。然而，统计方法和计算技术的进步带来了机遇为了解决问题，我们将制定所提出的联合模型并将其应用于 TEDDY 数据。可以进行统计推断来研究糖尿病相关抗体和其他抗体的变化如何根据目标 2，纵向危险因素与 1 型糖尿病诊断风险相关。提出的联合模型，将导出预测自身抗体发展的动态预测算法最后，在目标 3 中，我们将根据个人的历史数据确定随后患 1 型糖尿病的风险。使用各种诊断措施评估所提出的动态预测算法的准确性。我们期望所提出的联合模型将表现出比传统静态模型更好的性能所提出的研究可以使用基线特征或最后可用的测量值的生存模型。回答有关 1 型糖尿病自然史以及两者之间关系的关键研究问题纵向风险因素。