scholarly journals SiZer Map to investigate significant features of body-weight profile changes in HIV infected patients in the IeDEA Collaboration

2019 ◽  
Author(s):  
Jaroslaw Harezlak ◽  
Samiha Sarwat ◽  
Kara Wools-Kaloustian ◽  
Michael Schomaker ◽  
Eric Balestre ◽  
...  
Keyword(s):  
Weight Gain ◽  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Smoothing Splines ◽  
Real World Data ◽  
First Derivative ◽  
Persons Living With Hiv ◽  
Longitudinal Biomarker ◽  
Living With Hiv

AbstractObjectivesWe extend the method of Significant Zero Crossings of Derivatives (SiZer) to address within-subject correlations of repeatedly collected longitudinal biomarker data and the computational aspects of the methodology when analyzing massive biomarker databases. SiZer is a powerful visualization tool for exploring structures in curves by mapping areas where the first derivative is increasing, decreasing or does not change (plateau) thus exploring changes and normalization of biomarkers in the presence of therapy.MethodsWe propose a penalized spline SiZer (PS-SiZer) which can be expressed as a linear mixed model of the longitudinal biomarker process to account for irregularly collected data and within-subject correlations. Through simulations we show how sensitive PS-SiZer is in detecting existing features in longitudinal data versus existing versions of SiZer. In a real-world data analysis PS-SiZer maps are used to map areas where the first derivative of weight change after antiretroviral therapy (ART) start is significantly increasing, decreasing or does not change, thus exploring the durability of weight increase after the start of therapy. We use weight data repeatedly collected from persons living with HIV initiating ART in five regions in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) worldwide collaboration and compare the durability of weight gain between ART regimens containing and not containing the drug stavudine (d4T), which has been associated with shorter durability of weight gain.ResultsThrough simulations we show that the PS-SiZer is more accurate in detecting relevant features in longitudinal data than existing SiZer variants such as the local linear smoother (LL) SiZer and the SiZer with smoothing splines (SS-SiZer). In the illustration we include data from 185,010 persons living with HIV who started ART with a d4T (53.1%) versus non-d4T (46.9%) containing regimen. The largest difference in durability of weight gain identified by the SiZer maps was observed in Southern Africa where weight gain in patients treated with d4T-containing regimens lasted 52.4 weeks compared to 94.4 weeks for those with non-d4T-containing regimens. In the other regions, persons receiving d4T-containing regimens experienced weight gains lasting 51-61 weeks versus 59-77 weeks in those receiving non-d4T-based regimens.DiscussionPS-SiZer, a SiZer variant, can handle irregularly collected longitudinal data and within-subject correlations and is sensitive in detecting even subtle features in biomarker curves.

scholarly journals Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State

2020 ◽  
Vol 17 (23) ◽  
pp. 9055
Author(s):  
Carlos Díaz-Avalos ◽  
Pablo Juan ◽  
Somnath Chaudhuri ◽  
Marc Sáez ◽  
Laura Serra
Keyword(s):  
New York ◽  
Urban Areas ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Fine Particulate Matter ◽  
New York State ◽  
Atmospheric Pollutants ◽  
Real World Data ◽  
Pollutant Concentrations ◽  
Meteorological Elements

The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM2.5) and ozone (O3), atmospheric pollutants related to people’s mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Association of county perinatal resources and gestational weight gain in West Virginia, United States

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wilson A. Koech ◽  
Christa L. Lilly
Keyword(s):  
Weight Gain ◽  
West Virginia ◽  
Gestational Weight Gain ◽  
Mixed Model ◽  
Infant Health ◽  
Linear Mixed Model ◽  
Smoking During Pregnancy ◽  
Gestational Weight ◽  
Excessive Gestational Weight Gain ◽  
Significant Difference

Abstract Background Inappropriate (inadequate or excessive) gestational weight gain (GWG) is of great concern to maternal, fetal and infant health. Different maternal and fetal risk factors are associated with GWG, but little is known about a more distal risk factor: inadequate county-level perinatal resources. Therefore, the study aim was to investigate GWG in women living in counties with below average perinatal resources in comparison with their counterparts living in counties with above average perinatal resources. Methods Retrospective study of 406,792,010–2011 West Virginia births in 55 counties. The outcome was GWG and the main predictor was county perinatal resources. Hierarchical linear mixed model was used to investigate the association of county perinatal resources and GWG. Results County perinatal resources was associated with GWG (p = 0.009), controlling for important covariates. Below average county perinatal resources was not significantly associated with a decrease in mean GWG (M: − 5.29 lbs., 95% CI: − 13.94, 3.35, p = 0.2086), in comparison with counties with above average county perinatal resources. There was significant difference between average, and above average county perinatal resources (M: − 17.20 lbs., 95% CI: − 22.94, − 11.47, p < 0.0001), controlling for smoking during pregnancy and other covariates. Conclusions Average county perinatal resources was associated with reduced mean GWG relative to above average county perinatal resources, but not below average county perinatal resources. However, this could be due to the small number of counties with above average resources as the effect was in the hypothesized direction. This highlights one of the challenges in county perinatal resource studies.

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