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Fitting parametric random effects models in very large data sets with application to VHA national data
Gebregziabher Mulugeta,Egede Leonard,Gilbert Gregory E,Hunt Kelly
BMC Medical Research Methodology , 2012, DOI: 10.1186/1471-2288-12-163
Abstract: Background With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data. Data We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years. Methods and results The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous. Conclusion When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.
Simulated Estimates of Pre-Pregnancy and Gestational Diabetes Mellitus in the US: 1980 to 2008
Maria E. Mayorga, Odette S. Reifsnider, David M. Neyens, Mulugeta G. Gebregziabher, Kelly J. Hunt
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0073437
Abstract: Purpose To simulate national estimates of prepregnancy and gestational diabetes mellitus (GDM) in non-Hispanic white (NHW) and non-Hispanic black (NHB) women. Methods Prepregnancy diabetes and GDM were estimated as a function of age, race/ethnicity, and body mass index (BMI) using South Carolina live singleton births from 2004–2008. Diabetes risk was applied to a simulated population. Age, natality and BMI were assigned to women according to race- and age-specific US Census, Natality and National Health and Nutrition Examination Surveys (NHANES) data, respectively. Results From 1980–2008, estimated GDM prevalence increased from 4.11% to 6.80% [2.68% (95% CI 2.58%–2.78%)] and from 3.96% to 6.43% [2.47% (95% CI 2.39%–2.55%)] in NHW and NHB women, respectively. In NHW women prepregnancy diabetes prevalence increased 0.90% (95% CI 0.85%–0.95%) from 0.95% in 1980 to 1.85% in 2008. In NHB women from 1980 through 2008 estimated prepregnancy diabetes prevalence increased 1.51% (95% CI 1.44%–1.57%), from 1.66% to 3.16%. Conclusions Racial disparities in diabetes prevalence during pregnancy appear to stem from a higher prevalence of prepregnancy diabetes, but not GDM, in NHB than NHW.
Quantifying the Impact of Gestational Diabetes Mellitus, Maternal Weight and Race on Birthweight via Quantile Regression
Caitlyn N. Ellerbe, Mulugeta Gebregziabher, Jeffrey E. Korte, Jill Mauldin, Kelly J. Hunt
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0065017
Abstract: Background Quantile regression, a robust semi-parametric approach, was used to examine the impact of gestational diabetes mellitus (GDM) across birthweight quantiles with a focus on maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG). Methods Using linked birth certificate, inpatient hospital and prenatal claims data we examined live singleton births to non-Hispanic white (NHW, 135,119) and non-Hispanic black (NHB, 76,675) women in South Carolina who delivered 28–44 weeks gestation in 2004–2008. Results At a maternal BMI of 30 kg/m2 at the 90th quantile of birthweight, exposure to GDM was associated with birthweights 84 grams (95% CI 57, 112) higher in NHW and 132 grams (95% CI: 104, 161) higher in NHB. Results at the 50th quantile were 34 grams (95% CI: 17, 51) and 78 grams (95% CI: 56, 100), respectively. At a maternal GWG of 13.5 kg at the 90th quantile of birthweight, exposure to GDM was associated with birthweights 83 grams (95% CI: 57, 109) higher in NHW and 135 grams (95% CI: 103, 167) higher in NHB. Results at the 50th quantile were 55 grams (95% CI: 40, 71) and 69 grams (95% CI: 46, 92), respectively. Summary Our findings indicate that GDM, maternal prepregnancy BMI and GWG increase birthweight more in NHW and NHB infants who are already at the greatest risk of macrosomia or being large for gestational age (LGA), that is those at the 90th rather than the median of the birthweight distribution.
Marginalized Two Part Models for Generalized Gamma Family of Distributions
Delia C. Voronca,Mulugeta Gebregziabher,Valerie L. Durkalski,Lei Liu,Leonard E. Egede
Statistics , 2015,
Abstract: Positive continuous outcomes with a point mass at zero are prevalent in biomedical research. To model the point mass at zero and to provide marginalized covariate effect estimates, marginalized two part models (MTP) have been developed for outcomes with lognormal and log skew normal distributions. In this paper, we propose MTP models for outcomes from a generalized gamma (GG) family of distributions. In the proposed MTP-GG model, the conditional mean from a two-part model with a three-parameter GG distribution is parameterized to provide regression coefficients that have marginal interpretation. MTP-gamma and MTP-Weibull are developed as special cases of MTP-GG. We derive marginal covariate effect estimators from each model and through simulations assess their finite sample operating characteristics in terms of bias, standard errors, 95% coverage, and rate of convergence. We illustrate the models using data sets from The Medical Expenditure Survey (MEPS) and from a randomized trial of addictive disorders and we provide SAS code for implementation. The simulation results show that when the response distribution is unknown or mis-specified, which is usually the case in real data sets, the MTP-GG is preferable over other models.
Coexistence of Superconductivity and Ferromagnetism in Superconducting HoMo6S8  [PDF]
Tadesse Desta, Gebregziabher Kahsay
World Journal of Condensed Matter Physics (WJCMP) , 2015, DOI: 10.4236/wjcmp.2015.51004
Abstract: This work focuses on the theoretical investigation of the coexistence of superconductivity and ferromagnetism in the superconducting HoMo6S8. By developing a model Hamiltonian for the system and using the Green’s function formalism and equation of motion method, we have obtained expressions for superconducting transition temperature (Tc), magnetic order temperature (Tm), superconductivity order parameter (D) and magnetic order parameter (η). By employing the experimental and theoretical values of the parameters in the obtained expressions, phase diagrams of energy gap parameter versus transition temperature, superconducting transition temperature versus magnetic order parameter and magnetic order temperature versus magnetic order parameter are plotted separately. By combining the phase diagrams of superconducting transition temperature versus magnetic order parameter and magnetic order temperature versus magnetic order parameter, we have demonstrated the possible coexistence of superconductivity and ferromagnetism in superconducting HoMo6S8.<
Using quantile regression to investigate racial disparities in medication non-adherence
Mulugeta Gebregziabher, Cheryl P Lynch, Martina Mueller, Gregory E Gilbert, Carrae Echols, Yumin Zhao, Leonard E Egede
BMC Medical Research Methodology , 2011, DOI: 10.1186/1471-2288-11-88
Abstract: A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.Diabetes is a chronic debilitating illness that affects approximately 24 million people in the United States [1]. Medication adherence is an important component of good diabetes care and medication non-adherence is associated with poor glycemic control [2,3], increased health utilization [4,5], increased health care costs [6,7], and in
Reimaging Ethiopia through Destination Branding  [PDF]
Mulugeta Girma
American Journal of Industrial and Business Management (AJIBM) , 2016, DOI: 10.4236/ajibm.2016.62019
Abstract: As the name of a country is negatively seen due to certain unpleasant incidents, re-imaging is obviously important and Ethiopia is affected by early derogatory histories which force the modern readers and viewers conception to be shaped by stories of wars and natural disasters including famine crisis that highly affect the destination brands especially the re-imaging effort. On this regard, Ethiopia was analyzed from the context of the tourists and some concerned organization so as to identify the possibility of re-imaging the country by using destination branding practices. To meet the goal, the study used mixed research approach and samples of 368 respondents were selected randomly to fill the questionnaires and out of it, 316 of them were collected and analyzed using both descriptive and inferential statistics accordingly to test the hypothesis and reach the conclusions. The output reflects the destination marketing facts and insights in general, and recommendations are provided on how to re-image Ethiopia through destination branding which can be possible using branding techniques that could bring significant changes over stereotypes developed because of incidents that happen in the past.
Political Marketing: Exploring the Nexus between Theory and Practice in Ethiopia (Comparative Study between Ethiopian People’s Revolutionary Democratic Front and Coalition for Unity and Democratic Party)  [PDF]
Mulugeta Girma
Open Journal of Business and Management (OJBM) , 2016, DOI: 10.4236/ojbm.2016.42035
Abstract: The main purpose of the study was to explore the Nexus between Theory and Practice of political marketing in Ethiopia by examining the perceptions of members of EPRDF and CUDP political party. A mixed approach with 248 and 304 usable samples were collected from top members of two political parties randomly and relevant data were gathered, presented and analyzed using descriptive and inferential statistical techniques. The findings revealed that there was no formal marketing practice used by both parties and no department responsible for establishment of political marketing mix elements rather they seldom undertook conventionally by simply stand for what they believe, or focused on persuading voters to agree with their preplan ideas and policies which were relating with the selling concept and product concept.
Study of Upper Critical Magnetic Field of Superconducting HoMo6Se8  [PDF]
Tadesse Desta, Pooran Singh, Gebregziabher Kahsay
World Journal of Condensed Matter Physics (WJCMP) , 2015, DOI: 10.4236/wjcmp.2015.53013
Abstract: This work focuses on the study of mathematical aspects of upper critical magnetic field of superconducting HoMo6Se8. At zero external magnetic field, HoMo6Se8 was found to undergo a transition from the normal state to the superconducting state at 5.6 K and returned to a normal but magnetically ordered state between the temperature range of 0.3 K and 0.53 K. The main objective of this work is to show the temperature dependence of the upper critical magnetic field of superconducting HoMo6Se8 by using the Ginzburg-Landau (GL) phenomenological Equation. We found the direct relationship between the GL coherence length (ξGL) and penetration depth (λGL) with temperature. From the GL Equations and the results obtained for the GL coherence length, the expression for upper critical magnetic field (Hc2) is obtained for the superconducting HoMo6Se8. The result is plotted as a function of temperature. The graph shows the linear dependence of upper critical magnetic field (Hc2) with temperature (T) and our finding is in agreement with experimental observations.
Genotype by Environment Interaction Analysis for Tuber Yield of Potato (Solanum tuberosum L.) Using a GGE Biplot Method in Amhara Region, Ethiopia  [PDF]
Mulugeta Gedif, Dessalegn Yigzaw
Agricultural Sciences (AS) , 2014, DOI: 10.4236/as.2014.54027

Potato is one of the important crops grown in mid and high altitude areas of Ethiopia. Several potato genotypes have been introduced in different parts of this region. However, the stability and performance of these genotypes are not yet assessed. Therefore, the objective of this study was to determine the effect of genotype, environment and their interaction for tuber yield and identify stable potato genotypes. The study was conducted using eight potato genotypes in rainfed production season of years 2010 and 2011 at five potato growing locations in the region. Randomized complete block design with three replications was used for the experiment. Among the testing locations, the superior mean tuber yield (25.43 t/ha) was obtained at Adet while the inferior (13.89 t/ha) was at Injibara. Similarly, among the genotypes, CIP-396004.337 gave the highest mean tuber yield (25.66 t/ha), while CIP-395011.2 gave the lowest (17.78 t/ha). Combined ANOVA indicated that the main effects due to environments, genotypes and genotype by environment interaction were highly significant. The contribution of E, G and GEI to the total variation in tuber yield was about 47.11%, 8.83% and 44.07%, respectively. The GEI was further partitioned using GGE biplot model. The first two principal components obtained by singular value decomposition of the centered data of tuber yield explained 71.26% of the total variability caused by (G + GE). Out of these variations, PC1 and PC2 accounted 51.24% and 20.02% variability, respectively. GGE biplot view of this study identified Serinka as ideal

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