The paper introduces Neural ODE-LMM, a hybrid model that integrates a Neural Ordinary Differential Equation into a linear mixed‑effects framework to flexibly learn time‑varying associations between exposures and outcomes. By encoding covariate trajectories into a continuous‑time latent state, the method preserves standard likelihood inference while capturing complex, potentially cumulative effects without pre‑specifying functional forms. Simulations demonstrate accurate recovery of instantaneous and cumulative effects, and application to the 3C cohort uncovers BMI and fasting glucose trajectories linked to cognitive decline.
arXiv:2611.07937v1 Announce Type: cross Abstract: Functional data analysis provides a framework for studying time-varying associations between covariates and high-frequency biological signals. In dig...
arXiv:2610.07388v1 Announce Type: cross Abstract: Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the predict...
The paper introduces MEGPODE, a Bayesian nonparametric mixed‑effect ODE framework that models each subject’s dynamics as a combination of a shared population component and a subject‑specific deviation, both governed by Gaussian process priors. It avoids repeated ODE solves by representing latent trajectories with state‑space GP priors and penalizing derivative discrepancies on a finite time grid, enabling a single variational objective for joint inference of trajectories and vector fields. Experiments on heterogeneous oscillatory and biomedical ODE benchmarks show that MEGPODE outperforms strong baselines in recovering population‑level fields and predicting subject‑level trajectories.
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric...
arXiv:2610.34349v1 Announce Type: cross Abstract: In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates. We develop semiparametr...
The paper investigates how learned multimodal representations of high‑dimensional health data can fail to preserve causal information, even when trained to reconstruct the data. It establishes conditions under which a representation is sufficient for causal identification, shows that interventional mediation requires stricter preservation than counterfactual comparison, and proves that reconstruction‑based objectives cannot certify sufficiency. The authors decompose the error of cross‑fitted doubly robust estimators into a first‑order representation bias and a second‑order nuisance‑estimation remainder, demonstrating that coverage can deteriorate with larger samples if the representation is insufficient. They propose a practical workflow that combines modality‑specific variational autoencoders, cross‑fitted doubly robust estimation, locked phenotypes, selective pathway analysis, portable representations, and diagnostics for representation insufficiency, exposure leakage, and sensitivity, and validate it in simulations where a rare confounder’s omission leads to severely under‑covered indirect‑effect intervals.
arXiv:2607. 04647v1 Announce Type: cross Abstract: Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predictors and do not directly accommodate high-dimensional modalities such as images and text.
arXiv:2607. 21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points.
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
DeMMO is an interpretable framework that models longitudinal digital mobility outcomes (DMOs) across multiple diseases and outcomes using multi-task learning. It introduces a cross-disease, cross-outcome relation-learning mechanism that learns signed relationships from longitudinal DMO coefficient matrices, allowing selective information sharing even when disease cohorts lack shared participants. Evaluated on the Mobilise‑D dataset, DeMMO outperforms nine strong baselines and identifies reliable longitudinal DMO patterns for clinical validation.
arXiv:2606. 18535v1 Announce Type: cross Abstract: Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes.
GlucoFM is a lightweight foundation model for continuous glucose monitoring that aligns irregular CGM data to a 24‑hour grid and splits glucose dynamics into slow‑varying trend and short‑term deviation streams. Pre‑trained on over 109,000 hours of unlabeled recordings, it outperforms existing CGM‑specific models on seven phenotype‑classification tasks, improving average PR‑AUC by 4.1 points and enabling strong cross‑dataset transfer and few‑shot adaptation. When combined with meal, nutrition, and subject context, its frozen encoder delivers the lowest two‑hour postprandial glycemic response errors for trajectory, incremental AUC, peak rise, and peak timing metrics.