Inference for Odds Ratio Regression Models with Sparse Dependent Data

Biometrics ◽  
1998 ◽  
Vol 54 (1) ◽  
pp. 136 ◽  
Author(s):  
John J. Hanfelt ◽  
Kung-Yee Liang
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Dependent Data

Demographic factors associated with missed follow-up among solid tumor patients treated at a large multi-site academic institution

2020 ◽  
Vol 16 (32) ◽  
pp. 2635-2643
Author(s):  
Samantha L Freije ◽  
Jordan A Holmes ◽  
Saleh Rachidi ◽  
Susannah G Ellsworth ◽  
Richard C Zellars ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Odds Ratio ◽  
Regression Models ◽  
Tumor Patients ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Follow Up Care ◽  
Risk Patients ◽  
African American Race

Aim: To identify demographic predictors of patients who miss oncology follow-up, considering that missed follow-up has not been well studies in cancer patients. Methods: Patients with solid tumors diagnosed from 2007 to 2016 were analyzed (n = 16,080). Univariate and multivariable logistic regression models were constructed to examine predictors of missed follow-up. Results: Our study revealed that 21.2% of patients missed ≥1 follow-up appointment. African–American race (odds ratio [OR] 1.33; 95% CI: 1.17–1.51), Medicaid insurance (OR 1.59; 1.36–1.87), no insurance (OR 1.66; 1.32–2.10) and rural residence (OR 1.78; 1.49–2.13) were associated with missed follow-up. Conclusion: Many cancer patients miss follow-up, and inadequate follow-up may influence cancer outcomes. Further research is needed on how to address disparities in follow-up care in high-risk patients.

scholarly journals Interaction between dietary branched-chain amino acids and genetic risk score on the risk of type 2 diabetes in Chinese

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Weiqi Wang ◽  
Haiyang Jiang ◽  
Ziwei Zhang ◽  
Wei Duan ◽  
Tianshu Han ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Predisposition ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Regression Models ◽  
Fasting Glucose ◽  
Genetic Risk Score ◽  
Branched Chain Amino Acids ◽  
Branched Chain

Abstract Background and objectives Previous studies have found the important gene-diet interactions on type 2 diabetes (T2D) incident but have not followed branched-chain amino acids (BCAAs), even though they have shown heterogeneous effectiveness in diabetes-related factors. So in this study, we aim to investigate whether dietary BCAAs interact with the genetic predisposition in relation to T2D risk and fasting glucose in Chinese adults. Methods In a case-control study nested in the Harbin Cohort Study on Diet, Nutrition and Chronic Non-Communicable Diseases, we obtained data for 434 incident T2D cases and 434 controls matched by age and sex. An unweighted genetic risk score (GRS) was calculated for 25 T2D-related single nucleotide polymorphisms by summation of the number of risk alleles for T2D. Multivariate logistic regression models and general linear regression models were used to assess the interaction between dietary BCAAs and GRS on T2D risk and fasting glucose. Results Significant interactions were found between GRS and dietary BCAAs on T2D risk and fasting glucose (p for interaction = 0.001 and 0.004, respectively). Comparing with low GRS, the odds ratio of T2D in high GRS were 2.98 (95% CI 1.54–5.76) among those with the highest tertile of total BCAA intake but were non-significant among those with the lowest intake, corresponding to 0.39 (0.12) mmol/L versus − 0.07 (0.10) mmol/L fasting glucose elevation per tertile. Viewed differently, comparing extreme tertiles of dietary BCAAs, the odds ratio (95% CIs) of T2D risk were 0.46 (0.22–0.95), 2.22 (1.15–4.31), and 2.90 (1.54–5.47) (fasting glucose elevation per tertile: − 0.23 (0.10), 0.18 (0.10), and 0.26 (0.13) mmol/L) among participants with low, intermediate, and high genetic risk, respectively. Conclusions This study indicated that dietary BCAAs could amplify the genetic association with T2D risk and fasting glucose. Moreover, higher BCAA intake showed positive association with T2D when genetic predisposition was also high but changed to negative when genetic predisposition was low.

scholarly journals Sex Differences in the Association between Internet Usage and Overweight/Obesity: Evidence from a Nationally Representative Survey in Nepal

Sexes ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 132-143
Author(s):  
Juwel Rana ◽  
Md. Momin Islam ◽  
John Oldroyd ◽  
Nandeeta Samad ◽  
Rakibul Islam
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Internet Use ◽  
Adjusted Odds Ratio ◽  
Internet Usage ◽  
The Internet ◽  
Logistic Regression Models ◽  
Biological Sex ◽  
Obesity Interventions ◽  
Nationally Representative

Objective: To examine the associations between internet use and overweight/obesity in people aged 15–49 years in Nepal and the extent to which these associations differ by biological sex. Materials and methods: The study analyzed the nationally representative Nepal Demographic and Health Survey (NDHS) 2016 data. Multivariable ordinal logistic regression models were fitted to estimate the total effects of internet use (IU) in the last 12 months and frequency of internet use (FIU) in the last month on overweight/obesity adjusted for potential confounders. Results: Of the 10,380 participants, the prevalence of overweight/obesity by IU was 38% (95% confidence interval (CI): 35.9%, 40.1%) for males and 44.1% (95% CI: 41.6%, 46.6%) for female. The likelihood of overweight/obesity was significantly higher (adjusted odds ratio (aOR): 1.55; 95% CI: 1.40, 1.73; p < 0.001) among those participants who used the internet compared to the participants who did not use the internet in the last 12 months. Similar associations were observed when using the augmented measure of exposure-FIU in the last month. We observed the modification effect of sex in the associations of IU (p-difference < 0.001) and FIU (p-difference < 0.002) with overweight/obesity in Nepal. Conclusions: Our findings suggest that future overweight/obesity interventions in Nepal discourage unnecessary internet use, particularly among males.

Design effects for binary regression models fitted to dependent data

1993 ◽  
Vol 12 (13) ◽  
pp. 1259-1268 ◽  
Author(s):  
John M. Neuhaus ◽  
Mark R. Segal
Keyword(s):  
Regression Models ◽  
Dependent Data ◽  
Binary Regression ◽  
Design Effects

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

Living Arrangements and Sleep-Related Outcomes Among Older Adults in China

2019 ◽  
Vol 91 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Yen-Han Lee ◽  
Yen-Chang Chang ◽  
Timothy Chiang ◽  
Ching-Ti Liu ◽  
Mack Shelley
Keyword(s):  
Older Adults ◽  
Living Arrangements ◽  
Odds Ratio ◽  
Regression Models ◽  
Family Members ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Quality Of Sleep ◽  
Nationally Representative

It has been discussed previously that older adults’ living arrangements are associated with mortality. This study investigated the relationships between older adults’ living arrangements and sleep-related outcomes in China. The nationally representative sample included 4,731 participants who participated on two different occasions, with a total of 9,462 observations (2012 and 2014 waves). Panel logistic regression and panel ordinary least squares regression models were estimated with outcomes of sleep quality and average hours of sleep daily, respectively. Approximately 62% of individuals reported good quality of sleep. We observed that older adults who lived with family members had 17% greater odds of reporting good quality of sleep (adjusted odds ratio = 1.17, 95% confidence interval [1.03, 1.34], p < .05) and reported longer sleep duration daily (β = .334, standard error = .069, p < .01), compared with those who lived alone. Social support is needed to strengthen the residential relationship, especially with family members.

scholarly journals Movement Behaviors and Perceived Loneliness and Sadness within Alaskan Adolescents

2020 ◽  
Vol 17 (18) ◽  
pp. 6866
Author(s):  
Ryan D. Burns ◽  
Yang Bai ◽  
Christopher D. Pfledderer ◽  
Timothy A. Brusseau ◽  
Wonwoo Byun
Keyword(s):  
Mental Health ◽  
Odds Ratio ◽  
Regression Models ◽  
Movement Behavior ◽  
Youth Risk Behavior Survey ◽  
Association Analyses ◽  
Lower Odds ◽  
Logistic Regression Models ◽  
Race Ethnicity ◽  
Screen Use

Physical activity, screen use, and sleep are behaviors that integrate across the whole day. However, the accumulative influence of meeting recommendations for these 24-h movement behaviors on the mental health of Alaskan adolescents has not been examined. The purpose of this study was to examine the associations between movement behaviors, loneliness, and sadness within Alaskan adolescents. Data were obtained from the 2019 Alaska Youth Risk Behavior Survey (YRBS). The number of adolescents participating in the 2019 Alaska YRBS was 1897. Associations between meeting recommendations for movement behaviors with loneliness and sadness were examined using weighted logistic regression models, adjusted for age, sex, race/ethnicity, and body mass index (BMI). Approximately 5.0% of the sample met recommendations for all three movement behaviors. Meeting 2 or 3 movement behavior recommendations was associated with lower odds of loneliness (odds ratio (OR) range = 0.23 to 0.44, p < 0.01). Additionally, meeting 1 to 3 movement behavior recommendations was associated with lower odds of sadness (OR range = 0.29 to 0.52, p < 0.05). Joint association analyses determined that these relationships were primarily driven by meeting the sleep recommendation for loneliness and meeting the screen use recommendation for sadness. The results support use of multiple movement-based behavior programming to attenuate feelings of loneliness and sadness within Alaskan adolescents.

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