A Random-Effects Ordinal Regression Model for Multilevel Analysis

Biometrics ◽  
1994 ◽  
Vol 50 (4) ◽  
pp. 933 ◽  
Author(s):  
Donald Hedeker ◽  
Robert D. Gibbons
Keyword(s):  
Regression Model ◽  
Multilevel Analysis ◽  
Random Effects ◽  
Ordinal Regression ◽  
Ordinal Regression Model

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals ORDINAL REGRESSION FOR MODELLING THE FAMILY WELL-BEING AMONG THE MALAYSIANS

2021 ◽  
Vol 6 (2) ◽  
pp. 424-447
Author(s):  
Noor Azlin Muhammad Sapri ◽  
Kamarulzaman Ibrahim ◽  
Mohd Aftar Abu Bakar ◽  
Noratiqah Mohd Ariff
Keyword(s):  
Regression Model ◽  
Ordinal Data ◽  
Family Relationship ◽  
Well Being ◽  
Religious Practice ◽  
Ordinal Regression ◽  
Social Characteristics ◽  
The Family ◽  
The Status ◽  
Ordinal Regression Model

Background and Purpose: Understanding factors which affect the level of family well-being is important as it contributes to effective decision making among the policymakers to improve the family lives as well as to strengthen the family institution. Accordingly, this line of research is gaining attention. This study develops an ordinal regression model which identifies demographic, economic and social factors that are significant in explaining the status of family well-being.    Methodology: Data involving 2,808 respondents from a nationwide survey conducted by the National Population and Family Development Board of Malaysia in 2011 were used in this study. Ordinal regression model was implemented to describe the three levels of family well-being.   Findings: The national survey reported that high level of family well-being was experienced by 76.3 per cent of the respondents, followed by moderate (18.4%) and low (5.3%). The fitted ordinal regression model found that ethnic background, family relationship, community relationship, health and safety levels, economic situation of the family, religious practice, housing, and environment are significantly related to family well-being. Meanwhile, it was found that the level of income is not a significant factor in determining the level of family well-being.     Contributions: There are a limited number of studies on the application of ordinal regression for modelling the level of family well-being, particularly with covariates involving the demographic and social characteristics of the respondents. This study fills in the gap in the literature where the ordinal regression model provides useful information for policymakers to enhance the status of family well-being in Malaysia via various policy initiatives.   Keywords: Family well-being, Ordinal Regression Model, ordinal data, Proportional Odds Model.   Cite as: Muhammad Sapri, N. A., Ibrahim, K., Abu Bakar, M. A., & Mohd Ariff, N. (2021). Ordinal regression for modelling the family well-being among the Malaysians.  Journal of Nusantara Studies, 6(2), 424-447. http://dx.doi.org/10.24200/jonus.vol6iss2pp424-447

Abstract P442: The Association of Amyloid and Tau Burden With Centrum Semiovale Perivascular Spaces in Cerebral Amyloid Angiopathy

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Elif Gokcal ◽  
Alex A Becker ◽  
Mitchell J Horn ◽  
Alvin S Das ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
Regression Model ◽  
Cerebral Amyloid Angiopathy ◽  
Structural Mri ◽  
Ordinal Regression ◽  
White Matter Hyperintensity ◽  
Amyloid Angiopathy ◽  
Perivascular Spaces ◽  
Centrum Semiovale ◽  
Ordinal Regression Model ◽  
Cerebral Amyloid

Background: The mechanisms linking cerebral amyloid angiopathy (CAA) to enlarged perivascular spaces in centrum semiovale (CSO-EPVS) and whether other Alzheimer’s Disease (AD) pathologies might affect CSO-EPVS are unclear. We hypothesized that amyloid but not tau load would independently correlate with CSO-EPVS in CAA. Methods: Fifty prospectively enrolled nondemented probable CAA patients underwent high-resolution structural MRI, Pittsburgh compound B (PiB, for amyloid), and 18 F-flortaucipir (FTP, for tau) PET imaging. Microbleeds (all lobar, LMB) were counted and white matter hyperintensity volume (WMH) was quantified. CSO-EPVS were counted on T 2 -MRI sequence and graded using a previously validated scale (range 0-4). A multivariate ordinal regression model was used to assess the independent associations between CSO-EPVS and mean cortical amyloid as well as tau deposition, after adjusting for relevant covariates. Results: Patients had a mean age of 69.3±7.2. Age, sex, presence of hypertension, intracerebral hemorrhage (ICH), LMB counts, and WMH were not associated with CSO-EPVS grades (p>0.2 for all comparisons). Higher PiB uptake significantly correlated with increased CSO-EPVS (rho=0.45, p=0.001). Higher FTP showed a trend for correlation with CSO-EPVS (rho=0.26, p=0.069). In an ordinal regression model with CSO-EPVS grade as the dependent variable and both amyloid and tau levels included as predictors along with covariates presented above, the association of CSO-EPVS remained significant with higher PiB uptake (β=3.97, 95%CI 1.1-6.8, p=0.007) but not with FTP uptake (p=0.167). Conclusion: Results of this study suggest that CSO-EPVS is independently associated with amyloid but not with tau deposition in CAA. CSO-EPVS was not associated with age or classical vascular risk factors or presence of ICH. Our results support the view that vascular amyloid but not other AD pathologies such as tau might contribute to EPVS in patients with CAA.

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