An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data [PDF]
Annals of Applied Statistics 2010, Vol. 4, No. 3, 1517-1532, 2010 In many medical studies, patients are followed longitudinally and interest is on assessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable.
arxiv +1 more source
Conformal Predictions for Longitudinal Data [PDF]
arXiv, 2023 We introduce Longitudinal Predictive Conformal Inference (LPCI), a novel distribution-free conformal prediction algorithm for longitudinal data. Current conformal prediction approaches for time series data predominantly focus on the univariate setting, and thus lack cross-sectional coverage when applied individually to each time series in a ...
arxiv
Longitudinal Canonical Correlation Analysis [PDF]
arXiv, 2023 This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space.
arxiv
A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study [PDF]
arXiv, 2022 In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study.
arxiv
Longitudinal Self-Supervision for COVID-19 Pathology Quantification [PDF]
arXiv, 2022 Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been proposed to help radiologists automatically quantify COVID-19 pathologies on longitudinal CT scans.
arxiv
Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates [PDF]
arXiv, 2023 In linear models, omitting a covariate that is orthogonal to covariates in the model does not result in biased coefficient estimation. This in general does not hold for longitudinal data, where additional assumptions are needed to get unbiased coefficient estimation in addition to the orthogonality between omitted longitudinal covariates and ...
arxiv
A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules [PDF]
arXiv, 2022 A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses are often similar across multiple treatments. For example, the mean longitudinal trajectories for patients treated with an active drug and placebo may be very similar but ...
arxiv
Joint modelling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach [PDF]
Statistics in Medicine (2016) 35(3) 382-398, 2014 Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g ...
arxiv +1 more source
Evolution of the longitudinal structure function at small x [PDF]
Eur. Phys. J. Plus 129, (2014)19, 2014 We derive an approximation approach to evolution of the longitudinal structure function, by using a Laplace-transform method. We solve the master equation and derive the longitudinal structure function as a function of the initial condition $F_{L}(x,Q^{2}_{0})$ at small x. Our results are independent of the longitudinal coefficient functions and extend
arxiv +1 more source
A Unified Longitudinal Trajectory Dataset for Automated Vehicle [PDF]
Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs' longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of AV, however, often fall short in refinement, reliability, and completeness, hindering effective performance ...
arxiv +1 more source