风险预测对2型糖尿病（T2DM）预防非常重要。传统T2DM发病风险预测研究假定协变量不随时间改变，某些因素动态变化时预测精度会较低；因素交互作用研究一般假定协变量主效应和交互效应同阶，多因素分析时效应筛选性能会较差。课题组前期开展了依时风险评估与效应分层变量选择探索研究，本项目基于北京老年人群25年多维纵向数据，考虑身体状态、生活习惯和生化指标等多个因素依时性，以死亡作为T2DM发病竞争事件，建立依时竞争风险模型描述协变量动态变化与风险关系；考虑效应稀疏和分层准则，通过两步法对高维纵向数据降维，采用分层Lasso方法选择变量主效应和交互效应，准确筛选出老年人群T2DM发病风险因素与交互作用；通过随机模拟和随机抽样验证选择最优评估模型，采用Landmarking方法构建老年人群T2DM发病风险动态预测模型，提高预测精度。本研究将为T2DM等疾病发病风险因素与交互作用筛选和风险预测提供新方法。

Risk prediction is important for the prevention of Type 2 diabetes mellitus(T2DM). Most of the current risk prediction methods for T2DM are based on the assumption that the covariates are time-independent, but the prediction accuracy can be significantly degraded if some risk factors are highly dynamic in a longitudinal study. Most of studies on the interaction of risk factors for T2DM assume that main effect and interaction are on the same order, yet the performance of effect selection can be poor if there are many covariates in the study. The methods of statistical inference for the risk prediction model with time-dependent covariates and risk factor selection based on the effect hierarchy principle were proposed in our previous study. This project is based on the Beijing Longitudinal Study on the Elderly with 25-year follow-up period. Using time-dependent information including physical conditions, living habits and biochemical indexes should be an important consideration in the longitudinal study, and competing risk of the death should be a reasonable consideration in the elderly population. Therefore, the competing risks models with time-dependent covariates are proposed to describe the relation between the dynamic covariates and the risk of T2DM among the elderly in this project. Considering sparsity and hierarchy principles, the two steps method is used to reduce the dimension of the covariates in the longitudinal study. The hierarchical Lasso is presented to simultaneously select risk factors and interactions on the risk of T2DM in the elderly population. Comparisons among different risk assessment models and different effect selection methods are conducted by simulation studies and validation, and the best model is selected. The dynamic prediction model for the risk of T2DM among the elderly is obtained by using the Landmarking approach, and the prediction accuracy can be raised. This study proposes a new method for effect selection of risk factors, interaction analysis and risk prediction of T2DM and the similarly chronic non-communicable diseases in the elderly population.