神经常微分方程增强线性混合效应模型：估计时变协变量与标志物轨迹的复杂关联模式
Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory
另 1 家 · 查看机构详情
- Univ. Bordeaux（波尔多大学）
- Inserm（法国国家健康与医学研究院）
- INRIA（法国国家信息与自动化研究所）
- Vaccine Research Institute（疫苗研究所）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本研究提出Neural ODE-LMM模型，将Neural ODE嵌入LMM框架，通过最大化惩罚边际似然估计参数，在模拟和3C队列中成功揭示协变量与标志物轨迹的复杂关联模式。
中文摘要 AI 辅助
纵向队列研究产生的重复数据可用于评估暴露与健康结局之间的时变关联模式。经典线性混合效应模型（LMM）可适应多种关联模式，同时处理间隔不规则、部分观测的测量数据，但要求分析人员预先指定连接暴露史与结局的函数形式。我们提出了神经常微分方程-线性混合效应模型（Neural ODE-LMM），该模型在经典线性混合效应框架内嵌入了神经常微分方程（Neural ODE）：学习到的向量场将协变量轨迹编码为连续时间的潜在状态，该状态同时驱动固定效应和随机效应设计，同时保留标准LMM的观测模型。这既保留了基于经典似然的推断，又能灵活学习复杂的、可能是累积的协变量效应。所有参数通过最大化惩罚边际似然进行估计。为量化协变量效应，我们引入了反事实预测的对比方法，该方法比较不同协变量轨迹下的预期结局，并通过delta方法估计方差。在模拟实验中，该模型无需预先指定函数形式即可同时恢复瞬时效应和累积负担效应。将其应用于基于人群的7324名参与者的三城（3C）队列研究，该方法揭示了体重指数（BMI）和空腹血糖与认知衰退的轨迹依赖关联。
英文摘要
Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcomes. Classical linear mixed-effects models (LMMs) can accommodate a large variety of association patterns while accounting for the irregularly spaced, partially observed measurement. But they require the analyst to pre-specify the functional form linking the exposure history to the outcome. We propose the Neural ODE-LMM, which embeds a Neural Ordinary Differential Equation (Neural ODE) within the linear mixed-effects framework: a learned vector field encodes covariate trajectories into a continuous-time latent state that drives both the fixed- and random-effect design, while preserving the standard LMM observation model. This retains classical likelihood-based inference while learning complex, potentially cumulative, covariate effects flexibly. All parameters are estimated by maximising a penalised marginal likelihood. To quantify covariate effects, we introduce contrasts of counterfactual predictions that compare the expected outcome under alternative covariate trajectories with variance estimated via the delta method. In simulations, the model recovers both instantaneous and cumulative-burden effects without prior specification of the functional form. Applied to the Trois-Cités (3C) cohort, a population-based study of 7{,}324 participants, the method reveals trajectory-dependent associations of BMI and fasting glucose with cognitive decline.
