24 papers · ranked by Valyu relevance
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…
Eric Houngla Adjakossa, Ibrahim Sadissou, Mahouton Norbert Hounkonnou, Gregory Nuel + 1 more
'Gregory Nuel' 'Lourens J Waldorp'] In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more…
Feng Gao, J. Philip Miller, Chengjie Xiong, Jingqin Luo + 3 more
'Julia A. Beiser' 'Ling Chen' 'Mae O. Gordon'] Background Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over…
Maha Alsefri, Maria Sudell, Marta García-Fiñana, Ruwanthi Kolamunnage-Dona
'Ruwanthi Kolamunnage-Dona'] Background In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint…
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…
Ingrid Måge, Christina Steppeler, Ingunn Berget, Jan Erik Paulsen + 1 more
This paper presents a strategy for statistical analysis and interpretation of longitudinal intervention effects on bacterial communities. Data from such experiments often suffers from small sample size, high degree of irrelevant variation, and missing data points. Our strategy is a combination of multi-way…
Özgür Asar, Özlem İlk
Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modeling framework for multivariate marginal models to analyze multivariate…
Özgür Asar, Özlem İlk
Forecasting with longitudinal data has been rarely studied. Most of the available studies are for continuous response and all of them are for univariate response. In this study, we consider forecasting multivariate longitudinal binary data. Five different models including simple ones, univariate and multivariate…
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…
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…
Wenyi Lin, Michael C. Donohue, Philip Insel, Armin Schwartzman + 1 more
Alzheimer’s disease (AD) studies often collect longitudinal biomarker measures of multiple cohorts at different stages of disease and follow these biomarkers with a relatively short period of time. The heterogeneity of the longitudinal patterns of biomarkers can be ubiquitous across both individual trajectories and…
Antoine Bodein, Olivier Chapleur, Arnaud Droit, Kim-Anh Lê Cao
Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or…
Christian Geiser, Jacob Bishop, Ginger Lockhart, Saul Shiffman + 1 more
'Jerry L. Grenard'] Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear…
Seungmin Jahng, Phillip K. Wood
Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns…
Madan G. Kundu, Jaroslaw Harezlak
While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be uniform in these subgroups. That is, the timeslope and/or influence of covariates in longitudinal profile may vary among these different…
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks
Longitudinal mouse models are commonly used to study possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe…
Ariel I. Mundo, John R. Tipton, Timothy J. Muldoon
In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies…
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…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…
Authors not listed
Carotenoids are naturally occurring biomolecules with potent antioxidant activity that humans must obtain through their diet, as they cannot synthesize them endogenously. These pigments are naturally produced by plants and microbes, including red yeast cells that act as micro-factories, particularly genera such as…
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…