Influence diagnostics in mixed effects logistic regression models

Tapia, Alejandra; Leiva, Víctor; Díaz, María del Pilar; Giampaoli, Viviana

doi:10.1007/s11749-018-0613-3

Cited by 13 publications

(9 citation statements)

References 41 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…67 . Logistic mixed effects models 52 , 53 , 68 – 70 were fitted using the glmer function and performance of the fitted models compared and best model selected using the Akaike’s information criterion (AIC) 71 – 75 and Bayesian information criterion (BIC) 75 – 78 .…”

Section: Analysis and Resultsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Analysis and Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…The second one corresponds to local influence diagnostics that allows us to identify cases that, under small perturbations in the model or in the data, may cause disproportionate changes in the estimates of the model parameters; see details in, for example, Refs. [22,[24][25][26][27][28]30,[37][38][39].…”

Section: Data-influence Analytics In Mixed-effects Logistic Regressiomentioning

confidence: 99%

“…For the mixed-effects logistic regression model, we combine the global influence diagnostics proposed in [40] for the model with incomplete data and the local influence diagnostics presented in [24] for binary response variables, both supported in the Monte Carlo integration and sampling observations from the Metropolis-Hastings algorithm.…”

Section: Data-influence Analytics In Mixed-effects Logistic Regressiomentioning

confidence: 99%

“…There are several manners to make this validation in models for binary data [19]. Recent advances in model checking and diagnostics have been developed by several authors [20][21][22][23][24][25][26][27][28][29][30]. For more details and references regarding to statistical diagnostics, see Section 3.…”

Section: Introduction and Context Of The Empirical Applicationmentioning

confidence: 99%

See 1 more Smart Citation

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

et al. 2020

Self Cite

View full text Add to dashboard Cite

Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

show abstract

“…Residuals are well known and often used as measures of global influence and for detecting the model adequacy, 22,23 whereas the local influence technique is currently very popular. This technique allows us to evaluate the local effect of perturbations on the estimates of parameters and then to detect potentially influential cases in different models; see, for example, Santana et al 24 and Tapia et al 25 …”

Section: Introductionmentioning

confidence: 99%

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

Sánchez

Leiva

Galea

et al. 2020

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

The Birnbaum‐Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression models based on the BS distribution, which allows us to describe positive and asymmetric data when a quantile must be predicted using covariates. We use an approach based on a quantile parameterization to generate the model, permitting us to consider a similar framework to generalized linear models, providing wide flexibility. The methodology proposed includes a thorough study of theoretical properties and practical issues, such as maximum likelihood parameter estimation and diagnostic analytics based on local influence and residuals. The performance of the residuals is evaluated by simulations, whereas an illustrative example of income data is conducted using the methodology to show its potential for applications. The numerical results report an adequate performance of the approach to quantile regression, indicating that the BS distribution is a good modeling choice when dealing with data that have both positive support and asymmetry. The economic implications of our investigation are discussed in the final section. Hence, it can be a valuable addition to the tool kit of applied statisticians and econometricians.

show abstract

Influence diagnostics in mixed effects logistic regression models

Cited by 13 publications

References 41 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

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

Contact Info

Product

Resources

About