23 papers · ranked by Valyu relevance
Lynn Calman, Lisa Brunton, Alex Molassiotis
Background Longitudinal qualitative methods are becoming increasingly used in the health service research, but the method and challenges particular to health care settings are not well described in the literature.We reflect on the strategies used in a longitudinal qualitative study to explore the experience of symptoms…
Samuel W. Hawes, Andrew K. Littlefield, Daniel A. Lopez, Kenneth J. Sher + 22 more
'Kenneth J. Sher' 'Erin L. Thompson' 'Raul Gonzalez' 'Laika Aguinaldo' 'Ashley R. Adams' 'Mohammadreza Bayat' 'Amy L. Byrd' 'Luis FS Castro-de-Araujo' 'Anthony Dick' 'Steven F. Heeringa' 'Christine M. Kaiver' 'Sarah M. Lehman' 'Lin Li' 'Janosch Linkersdörfer' 'Thomas J. Maullin-Sapey' 'Michael C. Neale' 'Thomas E.…
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…
Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzen + 3 more
Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models have become the standard workhorse for statistical…
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…
Nicholas Tierney, Dianne Cook, Tania Prvan
Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a "spaghetti plot", where a line plot is drawn for each individual. When overlaid in…
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…
Åsa Audulv, Elisabeth O. C. Hall, Åsa Kneck, Thomas Westergren + 5 more
'Liv Fegran' 'Mona Kyndi Pedersen' 'Hanne Aagaard' 'Kristianna Lund Dam' 'Mette Spliid Ludvigsen'] Background Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in…
Rémi Colin-Chevalier, Frédéric Dutheil, Sébastien Cambier, Samuel Dewavrin + 7 more
'Samuel Dewavrin' 'Thomas Cornet' 'Julien Steven Baker' 'Bruno Pereira' 'Vasile Palade' 'Alireza Daneshkhah' 'Amin Hosseinian-Far' 'Samer A. Kharroubi'] Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts…
Andrew Ying
The paramount obstacle in longitudinal studies for causal inference is the complex "treatment-confounder feedback." Traditional methodologies for elucidating causal effects in longitudinal analyses are primarily based on the assumption that time moves in specific intervals or that changes in treatment occur discretely.…
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…
Nazanin Zounemat-Kermani, Matthew Richardson, Alen Faiz, Siyao Wang + 12 more
Many longitudinal omics studies contain only a small number of repeated measurements collected before, during, or after an intervention. Existing approaches, including mixed-effects models and generalized additive models, estimate temporal effects but do not generally provide a discrete representation of trajectory…
Luoting Zhuang, S. Park, Steven J. Skates, Ashley E. Prosper + 2 more
'Denise R. Aberle' 'William Hsu'] Abstract—Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective…
Lin Li, Mohammadreza Bayat, Timothy B. Hayes, Wesley K. Thompson + 2 more
This paper addresses the challenges of managing missing values within expansive longitudinal neu-roimaging datasets, using the specific example of data derived from the Adolescent Brain and Cog-nitive Development (ABCD^®^) study. The conventional listwise deletion method, while widely used, is not recommended due to…
Åsa Audulv, Thomas Westergren, Mette Spliid Ludvigsen, Mona Kyndi Pedersen + 5 more
'Mona Kyndi Pedersen' 'Liv Fegran' 'Elisabeth O. C. Hall' 'Hanne Aagaard' 'Nastasja Robstad' 'Åsa Kneck'] Background Qualitative longitudinal research (QLR) is an emerging methodology used in health research. The method literature states that the change in a phenomenon through time should be the focus of any QLR study…
Paul A. Smith, Wesley Yung
Business surveys are not generally considered to be longitudinal by design. However, the largest businesses are almost always included in each wave of recurrent surveys because they are essential for producing good estimates; and short‐period business surveys frequently make use of rotating panel designs to improve the…
Christie A. Bahlai, Easton R. White, Julia D. Perrone, Sarah Cusser + 1 more
A fundamental problem in ecology is understanding how to scale discoveries: from patterns we observe in the lab or the plot to the field or the region or bridging between short term observations to long term trends. At the core of these issues is the concept of trajectory—that is, when can we have reasonable assurance…
Benjamin F Trueman, Wendy H Krkošek, Graham A Gagnon
Orthophosphate can limit lead contamination of tap water, but its benefits are difficult to quantify since lead concentrations are so site-specific. Sentinel homes serviced by lead pipe are ideal for monitoring orthophosphate treatment, but best practices dictate the removal of lead once identified. The best sentinel…
Joseph Davies, David Pattison, Jonathan Hirst
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Seyma Alcicek, Piotr Put, Adam Kubrak, Fatih Celal Alcicek + 4 more
NMR relaxometry is an analytical method that provides information about the molecular environment, including even NMR “silent” molecules (spin-0), by analyzing the properties of NMR signals versus the magnitude of the longitudinal field. Conventionally, this technique has been performed at fields much higher than…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…
David Herrmann, Patrick Hodapp, Martin Starman, Pei-Chi Huang + 16 more
Analytical data in chemistry and other disciplines is usually generated in different formats and lacks common data and metadata standards that are necessary for a FAIR handling of research data. In the work presented herein, we describe a workflow that uses non-standardized, in some cases proprietary data formats from…