Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem...
Matrix variate longitudinal discrete data can arise in transcriptomics studies when the data are collected for N genes at r conditions over t time points, and thus, each observation
Transformer is the latest deep neural network (DNN) architecture for sequence data learning, which has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain...
Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve predi...
Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has...
Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above probl...
Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudina...
This paper focuses on a joint model to analyze longitudinal proportional and survival data. We utilize a logit transformation on the longitudinal proportional data and employ a partially linear mixed-effect model. With this model, we estimate the unk...
Tensor-structured multi-scale longitudinal data are quite prevalent in epidemiological, clinical, and other follow-up research. A key challenge in their statistical analysis is to simultaneously realize dimension reduction and longitudinal correlatio...
Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and c...
We are interested in detecting a departure from the baseline in a longitudinal analysis in the context of multiple organ dysfunction syndrome (MODS). In particular, we are given gene expression reads at two time points for a fixed number of genes and...
8 June 2022
Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world...
The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high-dimens...
Longitudinal data modeling is widely carried out using parametric methods. However, when the parametric model is misspecified, the obtained estimator might be severely biased and lead to erroneous conclusions. In this study, we propose a new estimati...
A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estim...
In longitudinal studies, a terminal event such as death typically halts data collection, and patients may exhibit marked changes as they approach the event. Existing methods face three related challenges: interpreting the effect of residual life on t...
Longitudinal studies routinely generate large-scale multivariate data in which multiple responses are measured repeatedly for each subject over time. Fitting the popular class of linear mixed-effects models to such data and obtaining maximum likeliho...
Rapid advancements in the field of deep learning have had a profound impact on a wide range of scientific studies. This paper incorporates the power of deep neural networks to learn complex relationships in longitudinal data. The novel generative app...
Research on the spread of dengue fever is typically measured periodically, producing longitudinally structured data. The varying coefficient model for longitudinal data allows the coefficient to vary as a smooth function of time. The data in this stu...
13 April 2026
We conducted a PRISMA-guided systematic review to summarize recent methodological advances in joint modeling. A PubMed search for English-language, peer-reviewed, full-text available articles published between 1 January 2019 and 30 January 2025 was c...
In the longitudinal data analysis we integrate flexible linear predictor link function and high-correlated predictor variables. Our approach uses B-splines for non-parametric part in the linear predictor component. A generalized estimation equation i...
While existing mixed-effects models address multivariate outcomes, nonlinearity, or non-normality separately, their joint integration remains limited in medical research. This study proposes a flexible semi-parametric multivariate mixed-effects model...
Data leakage is a major source of overoptimistic performance estimates in machine learning-based predictive modelling. In higher education, dropout models are increasingly used to support interventions, yet leakage may arise not only from technical m...
Synthetic datasets are increasingly used in education research for methodological validation, privacy-preserving data sharing, and reproducible equity analysis; however, most generative approaches prioritize marginal distributional similarity without...
15 September 2017
Administrative data are crucial to the “big data” revolution of social science and have played an important role in the development of child maltreatment research. These data are also of value to administrators, policy makers, and clinicians. The foc...
This study examines whether within-person changes in behavioral evaluation factors are associated with performance improvement using longitudinal personnel appraisal data from a Japanese firm (fiscal years 2019–2021, N = 218). Fourteen behavior...
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episo...
In clinical and epidemiological studies, when the time-to-event(s) and the longitudinal outcomes are associated, modelling them separately may give biased estimates. A joint modelling approach is required to obtain unbiased results and to evaluate th...
Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges...
When handling longitudinal data in regression models, we often encounter problems involving two interrelated response variables. These response variables may display an unknown curve shape in their relationship with one predictor variable, referred t...
The phenomenon of informative cluster size (ICS) emerges when the number of repeated measurements is correlated with the outcome variable. In such scenarios, the prevailing generalized estimating equation (GEE) method often yields biased estimates du...
Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer...
Fish year-class strength (YCS) has been estimated via longitudinal analysis of catch-at-age data and via catch-curve regression, but no study has compared the two approaches. The objective of this study was to compare YCS estimates between the two ap...
While headaches frequently occur in individuals with chronic kidney disease (CKD), there are few statistical evaluations of their connection to migraines in population-based studies. In this nationwide longitudinal follow-up study of Korean health ex...
Background: In predictive modelling, particularly in fields such as healthcare, the importance of understanding the model’s behaviour rivals, if not surpasses, that of discriminability. To this end, attention mechanisms have been included in de...
Predicting academic performance in face-to-face computing and software engineering courses is hindered by the limited availability of fine-grained process data. This study proposes STEAP, a camera-based static–temporal fusion framework that int...
Survival analysis plays a central role in medical research. Although the Cox proportional hazards (CoxPH) model remains the standard approach, machine learning and deep learning methods have been increasingly adopted. However, many published comparis...
Objectives: This study assessed the associations between long-term trajectories of percentage of energy from fat (PEF) and obesity among Chinese adults. Methods: Longitudinal data collected by the China Health and Nutrition Survey from 1991 to 2015 w...
Background: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, m...
Climate change is increasing the frequency of heat stress events in dairy farming, adversely affecting milk production, milk composition, cow behavior, and animal health. However, many previous studies relied on distant weather-station data and low-f...
Understanding the influence of weather on human well-being and mobility is essential to promoting healthier lifestyles. In this study we employ data collected from 151 participants over a continuous 30-day period in Switzerland to examine the effects...
The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from...
Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3...
Objective: To explore the trend and associated factors of neuropsychological development of infants and toddlers in China. Methods: A longitudinal study was conducted among 619 infants and toddlers (2914 person-times) aged 0 to 36 months from differe...
The increasing prominence of the problem of censored data in various fields has made studying how to perform parameter estimation and variable selection in censored mixed-effects models one of the hotspots of current research. In this paper, consider...
The mixed-effects model for repeated measures (MMRM) approach has been widely applied for longitudinal clinical trials. Many of the standard inference methods of MMRM could possibly lead to the inflation of type I error rates for the tests of treatme...
In an infinite-/super-population (SP) setup, regression analysis of longitudinal data, which involves repeated responses and covariates collected from a sample of independent individuals or correlated individuals belonging to a cluster such as a hous...
The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with...
Longitudinal metagenomics has been widely studied in the recent decade to provide valuable insight for understanding microbial dynamics. The correlation within each subject can be observed across repeated measurements. However, previous methods that...
Physiological dysregulation arising from chronic stress is a key mechanism linking psychosocial factors to long-term health outcomes, yet early identification typically relies on invasive or resource-intensive measurements. This study evaluates wheth...
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