21 papers · ranked by Valyu relevance
Xiaolei Lin, Robin Mermelstein, Donald Hedeker
Background Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate…
Jianchang Hu, Silke Szymczak
Precision medicine provides customized treatments to patients based on their characteristics and is a promising approach to improving treatment efficiency. Large scale omics data are useful for patient characterization, but often their measurements change over time, leading to longitudinal data. Random forest is one of…
José Ángel Martínez-Huertas, Emilio Ferrer
In this study we examine how a mixed-effects model with crossed random effects for individuals and variables estimates within- and between-variability in longitudinal multivariate trajectories from cohort-sequential designs. These designs are characterized by large proportions of planned missing data, and they usually…
Aglina Lika, Dimitris Rizopoulos, Michelle E Kruijshaar, Ans T van der Ploeg + 1 more
In medicine, multiple continuous outcomes are often repeatedly measured on each subject over time to assess disease severity. Usually, it is of interest to investigate the association between those outcomes, which may be measured at different time points, resulting in unbalanced data. The multivariate linear…
Wenbo Jing, Jeffrey S. Simonoff
In this paper, we propose a tree-based method called Multivariate RE-EM tree, which combines the regression tree and the linear mixed effects model for modeling multivariate response longitudinal or clustered data. The Multivariate RE-EM tree method estimates a populationlevel single tree structure that is driven by…
Sze Ming Lee, Yunxiao Chen, Tony Sit
longitudinal Data Authors: ['Sze Ming Lee' 'Yunxiao Chen' 'Tony Sit'] High-dimensional multivariate longitudinal data, which arise when many outcome variables are measured repeatedly over time, are becoming increasingly common in social, behavioral and health sciences. We propose a latent variable model for drawing…
Jin Liu
We present the R package nlpsem, which provides a comprehensive set of functions to assess longitudinal processes with individual measurement occasions within the structural equation modeling (SEM) framework. This package focuses on providing computational tools for nonlinear longitudinal models, particularly…
Ali Reza Taheriyoun, Allen Ross, Abolfazl Safikhani, Damoon Soudbakhsh + 1 more
Title: Graphical Abstract
Christos Chatzis, David Horner, Rasmus Bro, Ann-Marie Malby Schoos + 2 more
Temporal multivariate data is ubiquitous in many domains, for instance, being collected over time at planned visits (every few months/years) in longitudinal cohorts, or every few minutes/hours in challenge tests. The analysis of such data often focuses on revealing the underlying temporal patterns common across…
Anita Brobbey, Samuel Wiebe, Alberto Nettel-Aguirre, Colin Bruce Josephson + 3 more
generalized estimation equations Authors: ['Anita Brobbey' 'Samuel Wiebe' 'Alberto Nettel-Aguirre' 'Colin Bruce Josephson' 'Tyler Williamson' 'Lisa M Lix' 'Tolulope T. Sajobi'] Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two…
Duy-Thanh Vu, Duy-Cat Can, Christelle Schneuwly Diaz, Julien S. Bodelet + 18 more
Alzheimer’s Disease (AD) is the leading cause of dementia, affecting brain structure, function, cognition, and behaviour. While previous studies have linked brain regions to univariate outcomes (e.g., disease status), the relationship between brain-wide changes and multiple disease and behavioural outcomes of AD is…
Christopher Crawford, Jonathan Park, Sy‐Miin Chow, Anja F. Ernst + 2 more
'Vladas Pipiras' 'Zachary F. Fisher'] Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity…
Jinyuan Liu, Hanchang Cai, Zhichao Zhang, Jiawei Wang + 2 more
Variability in longitudinal health outcomes provides critical insight into disease dynamics, yet most statistical analyses continue to prioritize mean trajectories. This limitation is particularly consequential in psychiatric and mobile health (mHealth) research, where intensive repeated measurements increasingly…
Ethan R. Deyle, Gerald Pao, George Sugihara
The foundation of Empirical dynamic modeling (EDM) is in representing time-series data as the trajectory of a dynamic system in a multidimensional state space rather than as a collection of traces of individual variables changing through time. Takens’s theorem provides a rigorous basis for adopting this state-space…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Michael C. Freund, Ruiqi Chen, Gang Chen, Todd S. Braver
Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior…
Will Penny, Tom Sambrook, Louis Renoult
Factorial designs are a mainstay of the scientific paradigm, allowing the effects of multiple experimental factors and their interactions to be efficiently studied within a single experiment. In brain imaging, however, multivariate data analyses commonly proceed using multivariate decoding and we argue that the…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Authors not listed
This paper addresses the challenges in cell line development (CLD), the lengthy and ambiguous clone screening in upstream biopharmaceutical production. Typically, only a small subset of the later stages of CLD data is used for manually selecting lead clones. Addressing this issue, we introduce a multivariate data…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Authors not listed
A growth curve is S-shaped and typically contains three phases, namely, the lag phase, the exponential phase, and the saturation phase. Similarly, the time-series data of atmospheric CO2 levels over the past thousand years trend like the lag phase of growth until around 1940 and enter an exponential phase like curve…