Relationship Using multiple linear regression analysis and Bayesian network model analysis of factors affecting bone mineral density of residents in snowy region of Japan

2017 ◽  
Author(s):  
Teppei Suzuki ◽  
Tomoko Shimoda ◽  
Noriko Takahashi ◽  
Kaori Tsutsumi ◽  
Mina Samukawa ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Regression Analysis ◽  
Linear Regression ◽  
Bayesian Network ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Mineral Density ◽  
Bayesian Network Model ◽  
Factors Affecting

scholarly journals Anti-Inflammatory Effects of Low-Dose Glucocorticoids Compensate for Their Detrimental Effects on Bone Mineral Density in Patients with Rheumatoid Arthritis

2021 ◽  
Vol 10 (13) ◽  
pp. 2944
Author(s):  
Ji-Won Kim ◽  
Ju-Yang Jung ◽  
Hyoun-Ah Kim ◽  
Chang-Hee Suh
Keyword(s):  
Rheumatoid Arthritis ◽  
Bone Mineral Density ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Dose ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Annual Rate ◽  
Mineral Density

Objectives: This study aimed to provide reliable information on the impact of low-dose glucocorticoids (GCs) on the bone mineral density (BMD) of patients with rheumatoid arthritis (RA). Methods: This retrospective study enrolled 933 patients with RA who continued the consumption of GCs (GC group) and 100 patients who had discontinued consumption for >1 year (no-GC group). The BMD values were measured at baseline and follow-up, and the annual rate of change in BMD between the groups was compared using dual-energy X-ray absorptiometry. We used multiple linear regression analysis to identify the factors associated with changes in BMD. Results: The demographic characteristics and use of medical treatments affecting bone metabolism were similar between the two groups. Furthermore, there were no significant differences in the annual rate of changes in BMD and incidence of newly developed osteoporosis and incidental fractures between the two groups. Multiple linear regression analysis revealed that the disease activity score for 28 joints with erythrocyte sedimentation rate was the only factor affecting the annual rate of changes in BMD, and it was inversely proportional to changes in BMD. Conclusion: The benefits of GC therapy in attenuating inflammation compensate for the risk of osteoporosis if adequate measures to prevent bone loss are implemented in patients with RA.

Short-term, high-dose glucocorticoid treatment does not contribute to reduced bone mineral density in patients with multiple sclerosis

2015 ◽  
Vol 21 (12) ◽  
pp. 1557-1565 ◽  
Author(s):  
A Olsson ◽  
DB Oturai ◽  
PS Sørensen ◽  
PS Oturai ◽  
AB Oturai
Keyword(s):  
Multiple Sclerosis ◽  
Bone Mineral Density ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
High Dose ◽  
Mineral Density ◽  
Short Term

Background: Patients with multiple sclerosis (MS) are at increased risk of reduced bone mineral density (BMD). A contributing factor might be treatment with high-dose glucocorticoids (GCs). Objectives: The objective of this paper is to assess bone mass in patients with MS and evaluate the importance of short-term, high-dose GC treatment and other risk factors that affect BMD in patients with MS. Methods: A total of 260 patients with MS received short-term high-dose GC treatment and had their BMD measured by dual x-ray absorptiometry. BMD was compared to a healthy age-matched reference population ( Z-scores). Data regarding GCs, age, body mass index (BMI), serum 25(OH)D, disease duration and severity were collected retrospectively and analysed in a multiple linear regression analysis to evaluate the association between each risk factor and BMD. Results: Osteopenia was present in 38% and osteoporosis in 7% of the study population. Mean Z-score was significantly below zero, indicating a decreased BMD in our MS patients. Multiple linear regression analysis showed no significant association between GCs and BMD. In contrast, age, BMI and disease severity were independently associated with both lumbar and femoral BMD. Conclusion: Reduced BMD was prevalent in patients with MS. GC treatment appears not to be the primary underlying cause of secondary osteoporosis in MS patients.

scholarly journals Factors Affecting Company's Capability in Performing Integrated Reporting: An Empirical Evidence From Indonesian Companies

2018 ◽  
Vol 7 (2) ◽  
pp. 141
Author(s):  
Putu Sukma Kurniawan ◽  
Made Arie Wahyuni
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Institutional Ownership ◽  
Multiple Linear Regression Analysis ◽  
Managerial Ownership ◽  
Contrast Level ◽  
Integrated Reporting ◽  
Factors Affecting

<p>This study examines the factors that affect the company's capability to perform integrated reporting. The analysis used in testing the hypothesis is multiple linear regression analysis. Results show that company’s size has positive and significant connection and stakeholder’s pressure has negative and significant connection with the company’s capability in performing integrated reporting. In contrast, level of company’s profitability, company’s managerial ownership, and company’s institutional ownership did not have enough connection with company’s capability in performing integrated reporting.</p><p> </p>

scholarly journals Production Analysis of Green Mussel (Perna viridis) in Lampung Province

2020 ◽  
Vol 8 (2) ◽  
pp. 975
Author(s):  
Sulvina Sulvina ◽  
Zainal Abidin ◽  
Supono Supono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Influential Factors ◽  
Perna Viridis ◽  
Factors Affecting ◽  
Cultivation Process ◽  
Quantitative Descriptive

This study was conducted to find out factors affecting and level of mussel production, level of efficiency of using the tools and materials in cultivation process and whether the cultivation of mussels in Pasaran. This study was analyzed using Cobb-Douglass. The study were analyzed in quantitative descriptive, multiple linear regression analysis, and analysis of efficiency. The dependent variable (Y) is the result of production of green mussels cultivation and free variables are the number of bamboos (X 1), the amount of strap (X2), grouper (X3) and labor (X4). Mussel fisherman in Pasaran NPM with Px calculated to obtain the level of efficiency of each factors of production in messels cultivation. Studies show that the most influential factors production are variable bamboo, rope and labor. While the results of analysis the level of efficiency of using tools and materials is not efficient. The number of bamboo and labor should be reduced, because it tends to be a waste and not profitable either technically or economically. The value of the return to scale of 1.22 showed cultivation mussels are on increasing return to scale.

Wavelet and multiple linear regression analysis for identifying factors affecting particulate matter PM2.5 in Mumbai City, India

2019 ◽  
Vol 36 (10) ◽  
pp. 1750-1783 ◽  
Author(s):  
Vivekanand Venkataraman ◽  
Syed Usmanulla ◽  
Appaiah Sonnappa ◽  
Pratiksha Sadashiv ◽  
Suhaib Soofi Mohammed ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Measures ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Significant Factors

Purpose The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis. Design/methodology/approach In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable. Findings It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model. Research limitations/implications The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5. Practical implications Control measures such as control charts can be implemented for significant factors. Social implications Rules and regulations can be made more stringent on the factors. Originality/value The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

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