A mixed-effects regression model for three-level ordinal response data

2005 ◽  
Vol 24 (21) ◽  
pp. 3331-3345 ◽  
Author(s):  
Rema Raman ◽  
Donald Hedeker
Keyword(s):  
Regression Model ◽  
Mixed Effects ◽  
Response Data ◽  
Ordinal Response

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

Relationship between childhood obesity and socioeconomic status among primary school children in Costa Rica

2021 ◽  
pp. 1-24
Author(s):  
Tatiana Gamboa-Gamboa ◽  
Romain Fantin ◽  
Jeancarlo Cordoba ◽  
Ivannia Caravaca ◽  
Ingrid Gómez-Duarte
Keyword(s):  
Socioeconomic Status ◽  
Logistic Regression ◽  
Childhood Obesity ◽  
Costa Rica ◽  
Regression Model ◽  
Primary Schools ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Overweight And Obesity ◽  
Public And Private

Abstract Objective: This article analyzes the relationship between socioeconomic status and the prevalence of overweight and obesity in the primary school population in Costa Rica. Design: A National School Weight/Height Census was disseminated across Costa Rica in 2016. The percentage of children who were overweight or obese was calculated by sex, age, and socioeconomic indicators (type of institution: private, public, mix; type of geographic location: rural, urban; and the level of development of the district of residence: quartiles). A mixed effects multinomial logistic regression model and mixed effects logistic regression model were used to analyze the association between the prevalence of being overweight or obese and district socioeconomic status. Setting: The survey was carried out in public and private primary schools across Costa Rica in 2016. Participants: 347,366 students from 6 to 12 years old, enrolled in public and private primary schools. Results: The prevalence of overweight and obesity among children was 34.0%. Children in private schools were more likely to be overweight or obese than students in public schools (OR=1.10 [1.07, 1.13]). Additionally, children were less likely to be overweight or obese if attending a school in a district of the lowest socioeconomic quartile compared to the highest socioeconomic quartile (OR=0.79 [0.75, 0.83]), and in a rural area compared to the urban area (OR=0.92 [0.87, 0.97]). Conclusions: Childhood obesity in Costa Rica continues to be a public health problem. Prevalence of overweight and obesity in children was associated with indicators of higher socioeconomic status.

scholarly journals Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Farnoosh Abbas-Aghababazadeh ◽  
Pengcheng Lu ◽  
Brooke L. Fridley
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Functional Form ◽  
Cancer Cell Lines ◽  
Mixed Effects ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Modeling Framework ◽  
Response Data

Abstract Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.

P2778Geographic variability in the decline of the incidence of fatal and non-fatal stroke: a statewide study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J B Kostis ◽  
J Cabrera ◽  
S Zinonos ◽  
W J Kostis
Keyword(s):  
Risk Factors ◽  
Regression Model ◽  
Violent Crime ◽  
Low Income ◽  
Single Parent ◽  
Mixed Effects ◽  
Robert Wood Johnson Foundation ◽  
Stroke Incidence ◽  
Fatal Stroke ◽  
Physically Inactive

Abstract Background/Introduction There is scant information on the geographic variability in the rate of stroke incidence as it relates to the demographics, comorbidities, risk factors, and insurance type. Purpose/Methods County-level data on four modifiable groups of health factors including healthy behaviors, clinical care, physical environment, and socioeconomic conditions were obtained from the Robert Wood Johnson Foundation. The percentage of persons 65 years or older, smokers, physically inactive, obese, diabetics, heavy drinkers, college graduates, low income, unemployed, uninsured heads of single parent households, and residence in areas of violent crime was used in predicting fatal or non-fatal stroke. The counties were lumped into 5 categories based on similarities of the above characteristics. The incidence of fatal and non-fatal stroke was compared among the 5 county clusters using a mixed-effects regression model. Results The incidence of fatal and non-fatal stroke was significantly lower (p<0.0001) in cluster 3, where residents had higher income, were better educated, and were less likely to be unemployed, to live in single parent households, to have diabetes, to be obese, to smoke, to be physically inactive, or to live in communities with violent crime. The percentage of persons older than 65, violent crime rate, and obesity were identified as significant predictors of stroke using a mixed-effects regression model. Conclusions This study indicates that the incidence of stroke is higher in areas with older population, higher rate of obesity, and in regions with more violent crime. In order to improve health outcomes, preventive measures for stroke should address environmental factors in addition to the known cardiovascular risk factors. Acknowledgement/Funding Robert Wood Johnson Foundation

Sign in / Sign up

Export Citation Format

Share Document