A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data

Zhao, Yuanying; Xu, Dengke; Duan, Xingde; Du, Juan

doi:10.1080/00949655.2021.1998500

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…In this study, we used the logistic mixed effects model 54 to account for correlation between the repeated measurements in the BP data. The logistic mixed effects model achieves this by introducing a subject-specific random effect, which captures all unobserved subject-specific characteristics 69 , 70 . Secondly, this study used repeated binary BP data on 3809 Ghanaians to study the BP changes (high or low blood pressure) across the measurement periods as well determine risk factors associated with BP.…”

Section: Discussionmentioning

confidence: 99%

Mixed effects logistic regression analysis of blood pressure among Ghanaians and associated risk factors

Iddrisu,

Besing Karadaar,

Gurah Junior

et al. 2023

Sci Rep

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Blood pressure (BP) control is a global health issue with an increase in BP beyond the normal BP leading to different stages of hypertension in humans and hence the need to identify risk factors of BP for efficient and effective control. Multiple BP measurement have proven to provide BP readings close to the true BP status of the individual. In this study, we used multiple BP measurement data on 3809 Ghanaians to determine risk factors associated with BP. The data were obtained from World Health Organization study on Global AGEing and Adult Health. We defined high blood pressure (HBP) as $$\ge$$ ≥ 130/80 mmHg or normal as $$\le$$ ≤ 130/80 mmHg. We provide summary statistics and also used the Chi-Square test to assess significance of association between HBP versus risk factors of HBP. The aim of this study is to identify risk factors of BP using the mixed effects logistic regression model. Data were analyzed using R version 4.2.2. The results showed that the risk of high blood pressure (HBP) decreases across the three measurement periods. There is reduced risk (OR = 0.274, 95% CI = 0.2008, 0.405) of HBP among male participants relative to female participants. The risk (OR = 2.771, 95% CI = 1.8658, 4.1145) of HBP increased by 2.771-folds among those who are 60 years and above relative to those below the age of 60 years. Those whose work involves/requires vigorous exercise has 1.631-fold increase in the risk (OR = 1.631, 95% CI = 1.1151, 2.3854) of HBP relative to those whose work does not involve vigorous exercise. There is approximately 5-folds increased in the risk (OR = 4.896, 95% CI = 1.9535, 12.2268) of among those who have ever been diagnosed with diabetes. The results also revealed high risk (OR = 1.649, 95%CI = 1.1108, 2.4486) of HBP among those who have formal education. The risk (OR = 1.009, 95% CI = 1.0044, 1.0137) of HBP increases with increasing weight and a reduced risk (OR = 0.996, 95% CI = 0.9921, 0.9993) of HBP with increasing height. We found that sad experience, either mild, moderate or severe, is associated with a reduced risk of HBP. Those who have vegetable servings at least 2 cups per day have increased risk of HBP and those who have fruits servings at least 2 cups per day is associated with a reduced risk of HBP, however this is not statistically significant. To achieve success in BP control, programs should be designed with the aim of reducing weight, educate those with formal eduction on issues relating to HBP. Those whose work requires vigorous exercise are recommended to have regular check-ups to ensure that pressure build-up in the lungs is cleared. SBP is lower for women at young age but continue to increase after menopause as their BP increase becomes salt-sensitive. Hence there is need to give more attention to menopausal women so as to improve BP. Both young and old individuals are recommended to practice regular exercise since this has shown to reduce risk of being overweight or becoming diabetic and reduces the risk of HBP at yong age and old age. Also, to improve blood pressure control, programs for management of blood pressure or hypertension should focus more short stature individuals since such people are more likely to experience HBP.

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Section: Discussionmentioning

confidence: 99%

Mixed effects logistic regression analysis of blood pressure among Ghanaians and associated risk factors

Iddrisu,

Besing Karadaar,

Gurah Junior

et al. 2023

Sci Rep

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“…As a variant of Dirichlet process prior, the truncation approximation Dirichlet process with stick-breaking priors is widely incorporated into various mixed-effects models to specify the general distribution of random effects. For example, Tang and Duan [ 7 ] used this approach for a semiparametric Bayesian approach to generalized partial linear mixed model; Tang and zhao [ 8 ] used this approach for nonlinear reproductive dispersion mixed models; Zhao et al [ 9 ] used this approach for a semiparametric Bayesian approach to binomial distribution logistic mixed-effects model. In particular, Duan et al [ 10 ] used a truncated and centered Dirichlet process prior to specify random effects in semiparametric reproductive dispersion mixed model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

Tang

Duan

Zhao

2022

Entropy

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In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis–Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.

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“…There is continuing interest in developing mixed models for longitudinal binomial data [ 1 , 2 ]; however, these methods in the literature generally assume a fixed number of trials, and thus imply a negative association between the number of successes and the number of failures. As the number of trials is fixed, an increase in the number of successes implies a decrease in the number of failures, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study

Zhang

Yan

et al. 2022

Entropy

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Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures. This approach can accommodate both a random and zero number of trials. It can also accommodate overdispersion and zero inflation in the number of successes and the number of failures. An optimal estimation method for our model has been developed using the orthodox best linear unbiased predictors. Our approach not only provides robust inference against misspecified random effects distributions, but also consolidates the subject-specific and population-averaged inferences. The usefulness of our approach is illustrated with an analysis of quarterly bivariate count data of stock daily limit-ups and limit-downs.

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A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data

Cited by 3 publications

References 26 publications

Mixed effects logistic regression analysis of blood pressure among Ghanaians and associated risk factors

Mixed effects logistic regression analysis of blood pressure among Ghanaians and associated risk factors

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study

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