scholarly journals A note on the convergence rate of MCMC for robust Bayesian multivariate linear regression with proper priors

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Grant Backlund ◽  
James P. Hobert
Keyword(s):  
Linear Regression ◽  
Convergence Rate ◽  
Multivariate Linear Regression ◽  
Proper Priors

scholarly journals Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shouling Wu ◽  
Luli Xu ◽  
Mingyang Wu ◽  
Shuohua Chen ◽  
Youjie Wang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Multivariate Linear Regression ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Cross Sectional ◽  
Tyg Index ◽  
Longitudinal Association ◽  
The Cross

Abstract Background Triglyceride–glucose (TyG) index, a simple surrogate marker of insulin resistance, has been reported to be associated with arterial stiffness. However, previous studies were limited by the cross-sectional design. The purpose of this study was to explore the longitudinal association between TyG index and progression of arterial stiffness. Methods A total of 6028 participants were derived from the Kailuan study. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Arterial stiffness was measured using brachial-ankle pulse wave velocity (baPWV). Arterial stiffness progression was assessed by the annual growth rate of repeatedly measured baPWV. Multivariate linear regression models were used to estimate the cross-sectional association of TyG index with baPWV, and Cox proportional hazard models were used to investigate the longitudinal association between TyG index and the risk of arterial stiffness. Results Multivariate linear regression analyses showed that each one unit increase in the TyG index was associated with a 39 cm/s increment (95%CI, 29–48 cm/s, P < 0.001) in baseline baPWV and a 0.29 percent/year increment (95%CI, 0.17–0.42 percent/year, P < 0.001) in the annual growth rate of baPWV. During 26,839 person-years of follow-up, there were 883 incident cases with arterial stiffness. Participants in the highest quartile of TyG index had a 58% higher risk of arterial stiffness (HR, 1.58; 95%CI, 1.25–2.01, P < 0.001), as compared with those in the lowest quartile of TyG index. Additionally, restricted cubic spline analysis showed a significant dose–response relationship between TyG index and the risk of arterial stiffness (P non-linearity = 0.005). Conclusion Participants with a higher TyG index were more likely to have a higher risk of arterial stiffness. Subjects with a higher TyG index should be aware of the following risk of arterial stiffness progression, so as to establish lifestyle changes at an early stage.

scholarly journals 2011. Reaction of Clinicians to Positive Respiratory Viral Panels in Non-critically Ill Patients Without Bacterial Infection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S675-S675
Author(s):  
Jason C Gallagher ◽  
Sara Lee ◽  
Leah Rodriguez ◽  
Jacqueline Emily Von Bulow ◽  
Kaede Ota Sullivan
Keyword(s):  
Linear Regression ◽  
Bacterial Infection ◽  
Linear Regression Analysis ◽  
Solid Organ Transplantation ◽  
Retrospective Chart Review ◽  
Multivariate Linear Regression ◽  
Antibiotic Prescribing ◽  
Antibiotic Administration ◽  
Multivariate Linear Regression Analysis ◽  
Solid Organ

Abstract Background Respiratory viral panels (RVPs) can detect multiple viral pathogens and give clinicians diagnostic confidence to discontinue antibiotics. However, relatively little is known about how these tests influence antibiotic prescribing in hospital settings. Methods This was a 26-month retrospective chart review of patients with positive RVPs. Hospitalized adults receiving antibiotics at the time of the RVP were included. Exclusion criteria were: ICU care, solid-organ transplantation (SOT), positive RVP for influenza, positive bacterial cultures, and antibiotic administration for bacterial infection (e.g., cellulitis). A multivariate linear regression model was created to investigate associations with longer antibiotic use after a positive RVP. Results 1,346 patients were screened and 242 met inclusion criteria. Primary reasons for exclusion were SOT, ICU, and influenza diagnosis. Patients were a median age of 60.5 years [IQR 51,70] and 35.5% were men. The median length of stay (LOS) was 4 days [IQR 3.6]. 233 patients (6.3%) had chest radiology performed, of which 71 (30.4%) had possible pneumonia noted. 50 (20.7%) were immunocompromised (IC). 199 (82.2%) had a history of pulmonary disease, most commonly COPD. Rhinovirus was isolated in 156 patients (64.5%), followed by metapneumovirus (35, 14.9%) and RSV (32, 13.3%). Antibiotics were given for a median total of 3 days [IQR 3.6]; they were discontinued within 24 hours of the RVP result in 107 patients (44.2%). Conclusion In this population of patients with viral infection and no discernable bacterial infection, 44.2% of patients had antibiotics discontinued within 24 hours of RVP results. On multivariate linear regression analysis, younger age, longer LOS, and IC status were associated with longer antibiotic duration after a positive RVP. A comparison with patients with negative RVP results could reveal if the test prompted discontinuation. Disclosures All authors: No reported disclosures.

Correlation between FA and ADC, number and length of the periprostatic neurovascular fibers

2021 ◽  
pp. 039156032110637
Author(s):  
Valerio Di Paola ◽  
Angelo Totaro ◽  
Giacomo Avesani ◽  
Benedetta Gui ◽  
Andrea Boni ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Negative Correlation ◽  
Multivariate Regression ◽  
Linear Regression Analysis ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Analysis ◽  
Independent Variables ◽  
Fiber Tracts ◽  
Good Agreement

Purpose: Our aim was to explore the relation between FA and ADC, number and length of the periprostatic neurovascular fibers (PNF) by means of 1.5 T Diffusion Tensor Imaging (DTI) imaging through a multivariate linear regression analysis model. Methods: For this retrospective study, 56 patients (mean age 63.5 years), who underwent 1.5-T prostate MRI, including DTI, were enrolled between October 2014 and December 2018. Multivariate regression analysis was performed to evaluate the statistically significant correlation between FA values (dependent variable) and ADC, the number and the length of PNF (independent variables), if p-value <0.05. A value of 0.5 indicated poor agreement; 0.5–0.75, moderate agreement; 0.75–0.9, good agreement; 0.61–0.80, good agreement; and 0.9–1.00, excellent agreement. Results: The overall fit of the multivariate regression model was excellent, with R2 value of 0.9445 ( R2 adjusted 0.9412; p < 0.0001). Multivariate linear regression analysis showed a statistically significant correlation ( p < 0.05) for all the three independent variables. The r partial value was −0.9612 for ADC values ( p < 0.0001), suggesting a strong negative correlation, 0.4317 for the number of fiber tracts ( p < 0.001), suggesting a moderate positive correlation, and −0.306 for the length of the fiber tracts ( p < 0.05), suggesting a weak negative correlation. Conclusions: Our multivariate linear regression model has demonstrated a statistically significant correlation between FA values of PNF with other DTI parameters, in particular with ADC.

scholarly journals Short period PM2.5 prediction based on multivariate linear regression model

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201011 ◽  
Author(s):  
Rui Zhao ◽  
Xinxin Gu ◽  
Bing Xue ◽  
Jianqiang Zhang ◽  
Wanxia Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Short Period

Life satisfaction and its relationship with spiritual well-being and religious practice in Iranian adolescent girls

2017 ◽  
Vol 31 (6) ◽  
Author(s):  
Fariba Alizadeh Sharajabad ◽  
Sakineh Mohammad-Alizadeh Charandabi ◽  
Mojgan Mirghafourvand
Keyword(s):  
Life Satisfaction ◽  
Linear Regression ◽  
Adolescent Girls ◽  
Well Being ◽  
Religious Practice ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Analysis ◽  
Income Status ◽  
Spiritual Well Being ◽  
The Mean

Abstract Introduction During recent years, special attention has been given to spiritual well-being and religious practice in the field of health. This study aimed to determine the predictors of life satisfaction among adolescent girls in Tabriz, Iran, 2015. Materials and methods This cross-sectional study was conducted on 520 female students studying in high schools who were selected using the cluster sampling method. Data collection was carried out through the questionnaires of socio-demographic characteristics, spiritual well-being (SWBS), religious practice (Arcury and colleagues) and life satisfaction (SWLS). Multivariate linear regression model was used for data analysis. Results The mean score of life satisfaction was 22.0 (SD: 6.0) from the attainable score of 5–35. The mean score (SD) of spiritual well-being was 90.2 (16.2), ranging from 20 to 120. The mean score of the religious practice was 32.1 (10.5) out of the achievable score range of 0–60. Multivariate linear regression analysis showed that existential well-being and sufficiency of income for expenses were predictors of life satisfaction and they explained 41.8% of the variance in the life satisfaction score. Conclusion The findings of the present study confirm the importance of existential well-being and a modifiable variable (sufficiency of income) in the life satisfaction. Thus, it is necessary to provide strategies to promote spirituality and improve the income status for improving adolescents’ life satisfaction.

scholarly journals Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors. Aims: The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district. Data and methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district. Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou. Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

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