Open Access. Powered by Scholars. Published by Universities.®
Longitudinal Data Analysis and Time Series Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Epidemiology (2)
- 3.3 HEALTH SCIENCES (1)
- Activities of daily living (1)
- Aged (1)
- Bayesian nonilnear hierchical model (1)
-
- Bioinformatics (1)
- Body mass index (1)
- Cohort studies (1)
- Diagnosis (1)
- Female (1)
- Gait (1)
- Humans (1)
- Infection control (1)
- Infection control nurse (1)
- Infection response (1)
- Influenza (1)
- Longitudinal data analysis (1)
- Longitudinal studies (1)
- Male (1)
- Middle aged (1)
- Motor activity (1)
- Muscle strength (1)
- Obesity (1)
- Osteoarthritis (1)
- Pandemic (1)
- Personal protective equipment (1)
- Physiology (1)
- Physiopathology (1)
- Prospective studies (1)
- Public and environmental health (1)
- Publication
- Publication Type
Articles 1 - 4 of 4
Full-Text Articles in Longitudinal Data Analysis and Time Series
Dynapenic Obesity And The Effect On Long-Term Physical Function And Quality Of Life: Data From The Osteoarthritis Initiative, John A. Batsis, Alicia J. Zbehlik, Dawna Pidgeon, Stephen J. Bartels
Dynapenic Obesity And The Effect On Long-Term Physical Function And Quality Of Life: Data From The Osteoarthritis Initiative, John A. Batsis, Alicia J. Zbehlik, Dawna Pidgeon, Stephen J. Bartels
Dartmouth Scholarship
Obesity is associated with functional impairment, institutionalization, and increased mortality risk in elders. Dynapenia is defined as reduced muscle strength and is a known independent predictor of adverse events and disability. The synergy between dynapenia and obesity leads to worse outcomes than either independently. We identified the impact of dynapenic obesity in a cohort at risk for and with knee osteoarthritis on function.
Preparedness Of Hospitals In The Republic Of Ireland For An Influenza Pandemic, An Infection Control Perspective, Mary Reidy, Fiona Ryan, Dervla Hogan, Seán Lacey, Claire Buckley
Preparedness Of Hospitals In The Republic Of Ireland For An Influenza Pandemic, An Infection Control Perspective, Mary Reidy, Fiona Ryan, Dervla Hogan, Seán Lacey, Claire Buckley
Department of Mathematics Publications
When an influenza pandemic occurs most of the population is susceptible and attack rates can range as high as 40–50 %. The most important failure in pandemic planning is the lack of standards or guidelines regarding what it means to be ‘prepared’. The aim of this study was to assess the preparedness of acute hospitals in the Republic of Ireland for an influenza pandemic from an infection control perspective.
Nonlinear Hierarchical Models For Longitudinal Experimental Infection Studies, Michael David Singleton
Nonlinear Hierarchical Models For Longitudinal Experimental Infection Studies, Michael David Singleton
Theses and Dissertations--Epidemiology and Biostatistics
Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods …
Case Studies In Evaluating Time Series Prediction Models Using The Relative Mean Absolute Error, Nicholas G. Reich, Justin Lessler, Krzysztof Sakrejda, Stephen A. Lauer, Sopon Iamsirithaworn, Derek A T Cummings
Case Studies In Evaluating Time Series Prediction Models Using The Relative Mean Absolute Error, Nicholas G. Reich, Justin Lessler, Krzysztof Sakrejda, Stephen A. Lauer, Sopon Iamsirithaworn, Derek A T Cummings
Nicholas G Reich
Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this paper, we present a framework for evaluating time series predictions that emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naive reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this …