Publication Date: 2025
International Journal for Educational and Vocational Guidance (15731782)
This study adopted a person-centered approach to examine the perceived employability profiles of undergraduate students. Three profiles were extracted: confident, unconfident, and semiconfident. The relationships among these profiles with adaptivity, career adaptability, and adapting were assessed. The data were collected from 1025 undergraduate students. Higher levels of career adaptability, adaptivity, and adapting predicted membership in the confident profile, while lower levels were associated with the unconfident profile. None of the variables predicted membership in the semiconfident profile. The findings revealed that self-belief and perceived labor market status are important in students’ perceived employability. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Publication Date: 2025
Statistical Analysis and Data Mining (19321864)18(1)
The marginal regression model is convenient for analyzing correlated responses, including repeated measures and longitudinal data. This paper proposes a robust marginal linear model for analyzing a vector of univariate responses with correlated components by incorporating an innovative multivariate Huber distribution. It employs a flexible parameterization using modified Cholesky decomposition, provides a convenient approach for estimating the covariance matrix, and allows for subject-varying the tuning parameter. Our research introduces a method for estimating parameters by employing the exact likelihood function through the Hamiltonian Monte Carlo algorithm. To highlight the advantage of our model, we carried out a simulation experiment and reanalyzed two real-world case studies in the health and economics fields. The results indicate that our model offers a more robust analysis by assigning appropriate weights to extreme observations, thereby handling outliers more effectively than traditional models. © 2025 Wiley Periodicals LLC.
Publication Date: 2024
Statistical Modelling (1471082X)24(1)pp. 58-81
This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence. © 2022 The Author(s).
Publication Date: 2024
Communications in Statistics Part B: Simulation and Computation (15324141)53(9)pp. 4554-4567
A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior. © 2022 Taylor & Francis Group, LLC.
Publication Date: 2024
Journal of Computational and Applied Mathematics (03770427)438
This paper presents innovative strategies for correlated data analysis using dynamic mixed effects models to account for the autoregressive conditional-heteroscedasticity of innovations and cross-sectional dependence. In a multivariate setting, the traditional modeling scheme was extended to time-varying components of variances and covariances by initiating the heterogeneous GARCH model. We propose a Bayesian semi-parametric approach by utilizing the centered Dirichlet process as priors for the heterogeneity effects involved in the mean structure. We present a stochastic clustering strategy to distinguish similar patterns of covariates. The Hamiltonian Monte Carlo technique is used to ease the computation of time-series equations for variances and covariances with higher efficiency. We conduct comprehensive simulation studies to examine model features, especially in cluster-wise settings. We adopt the maximal overlap discrete wavelet transforms to isolate short- and long-run sample information for practical applications. The transformation facilitates analyzing the effect of high-frequency or trend variation in macroeconomic factors. We illustrate the advantage of our proposed modeling methodology in the empirical studies by investigating the short-run impacts of macroeconomic events on economic growth and its prediction accounting for the unexpected volatility in the financial market of some selected countries. © 2023 Elsevier B.V.
Publication Date: 2024
Annals of Human Genetics (00034800)88(3)pp. 212-246
Objective: The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. Methods: This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. Results: Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. Conclusions: By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined. © 2023 University College London (UCL) and John Wiley & Sons Ltd.
Publication Date: 2023
IET Science, Measurement and Technology (17518822)17(9)pp. 351-360
Low-frequency noise, generated inherently by the number or mobility fluctuation of carriers, is a crucial concern for the design of analog and digital circuits. Unified modelling based on experimental validation of near-DC noise in amplifiers is a long-standing open problem. This article develops a model for low-frequency noise by deriving new bounds for carrier capturing and releasing. According to the proposed model, a measurement system is suggested that operates in a wide frequency range and even at very low frequencies. The system is noise-tolerant, since the amplifier is selected based on acceptable noise levels. Among the advantages are the independence from specialized structural noise models for each component and the low cost of the measurement system. The evaluation results show that the proposed method leads to a promising improvement in the low-frequency noise measuring and is superior to conventional models in the normalized root mean square error indicator. Findings reveal that the proposed measurement method can estimate the flicker noise around the DC frequency, and the proposed model agrees reasonably with the proposed measurement circuit. © 2023 The Authors. IET Science, Measurement & Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2023
Methodology and Computing in Applied Probability (13875841)25(3)
Several extensions of the familiar Dirichlet process have been widely investigated to nonparametric Bayesian model fittings parallel with appealing subsequent studies on their particular properties. This paper presents an explicit form for the joint distribution of drawn samples from the beta two-parameter process using an extension of stick-breaking construction. In particular, we evaluate the joint distribution of a random sequence for a specific process case and compare it with the Blackwell-MacQueen process. We obtain moments of the beta two-parameter process and present a formula for the number of distinct values in the sample. We establish the precision ratio and explore its effect on this number. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2023
Journal of Computational and Applied Mathematics (03770427)419
Extensions of modeling continuously bounded and positive responses in regression contexts are often prominent. Most regression techniques incorporate a response transformation to improve underlying model fittings. A further challenge is, however, demanding to promise the transformation success. It motivated us to introduce a novel modeling strategy using the generalized Johnson system of transformations. We propose joint regression modeling of the median and precision parameters by exploiting various invertible transformations and link functions. It offers a convenient alternative to several regression models, including the normal, the popular Beta for bounded, and the log-symmetric for positive responses. Other attractive features include the iteratively reweighted-least-squares algorithm (IRLS) development to facilitate computational aspects and robust residual diagnostics to detect outlying points. Monte Carlo simulations and analysis of three real-life data sets illustrate the usefulness of our modeling strategy. © 2022 Elsevier B.V.
Publication Date: 2023
Computational Statistics and Data Analysis (01679473)188
A time-varying multivariate integer-valued autoregressive of order one (tvMINAR(1)) model is introduced for the non-stationary time series of correlated counts when under-reporting is likely present. A non-diagonal autoregression probability network is structured to preserve the cross-correlation of multivariate series, provide a necessary condition to ease model-fittings computations, and derive the full likelihood using the Viterbi algorithm. The motivating construction applies to fully under-reported counts that rely on a mixture presentation of the random thinning operator. Simulation studies are conducted to examine the proposed model, and the analysis of COVID-19 daily cases is accomplished to highlight its usefulness in applications. Finally, the comparison of models is presented using the posterior predictive checking method. © 2023 Elsevier B.V.
Publication Date: 2022
Statistical Modelling (1471082X)22(4)pp. 327-348
This article introduces a flexible modelling strategy to extend the familiar mixed-effects models for analysing longitudinal responses in the multivariate setting. By initiating a flexible multivariate multimodal distribution, this strategy relaxes the imposed normality assumption of related random-effects. We use copulas to construct a multimodal form of elliptical distributions. It can deal with the multimodality of responses and the non-linearity of dependence structure. Moreover, the proposed model can flexibly accommodate clustered subject-effects for multiple longitudinal measurements. It is much useful when several subpopulations exist but cannot be directly identifiable. Since the implied marginal distribution is not in the closed form, to approximate the associated likelihood functions, we suggest a computational methodology based on the Gauss–Hermite quadrature that consequently enables us to implement standard optimization techniques. We conduct a simulation study to highlight the main properties of the theoretical part and make a comparison with regular mixture distributions. Results confirm that the new strategy deserves to receive attention in practice. We illustrate the usefulness of our model by the analysis of a real-life dataset taken from a low back pain study. © 2020 Statistical Modeling Society.
Publication Date: 2022
Journal of Multivariate Analysis (0047259X)187
Conventional linear mixed-effects modeling is routinely challenging when the validity of necessary assumptions is suspicious. In particular, robustifying model fitting is appealing in the presence of potential outlying points. This paper introduces a robust regression methodology in a parametric setting by constructing a novel multivariate skew-Huber distribution for longitudinal data with heavy-tails and skewed structures. Unlike preceding studies, our model allows for jointly estimating the tuning parameter, which controls the impact of outliers, with all other parameters using an undemanding computational algorithm. Moreover, by promoting an unconstrained parameterization through the modified Cholesky decomposition, the estimate of variance–covariance components can be merely accessible. We also present a spline mixed model to account for the covariate effect. To highlight the usefulness of our methodology, we conducted a simulation study and analyzed a data set collected on type 2 diabetic patients with microalbuminuria over a 6-year prospective cohort study. Findings show that our proposed robust model leads to convincing conclusions in empirical studies. © 2021 Elsevier Inc.
Publication Date: 2022
Journal Of The Iranian Statistical Society (17264057)21(2)pp. 111-132
Bayesian nonparametric inference is increasingly demanding in statistical modeling due to incorporating flexible prior processes in complex data analysis. This paper represents the Polya urn scheme for the generalized Dirichlet process (GDP). It utilizes the partition analysis to construct the joint distribution of a random sample from the GDP as a mixture prior distribution of countable components. Using permutation theory, we present the components’ weights in a computationally accessible manner to make the resulting joint prior equation applicable. The advantages of our findings include tractable algebraic operations that lead to closed-form equations. The paper recommends the Polya urn Gibbs sampler algorithm, derive full conditional posterior distributions, and as an illustration, implement the algorithm for fitting some popular statistical models in nonparametric Bayesian settings. © 2023, (Iranian Statistical Society). All Rights Reserved.
Publication Date: 2022
Journal of Multivariate Analysis (0047259X)190
Multivariate analysis of multiple correlated responses is often challenging due to the complex data structure. For analyzing such responses, this paper presents a pragmatic multivariate mixed-effects model. The model can flexibly accommodate both symmetric and asymmetric structures by utilizing a novel multivariate transformed distribution belonging to the family of elliptical distributions. It also offers a convenient alternative to most multivariate mixed models for analyzing bounded and positive correlated multivariate responses. The model is based on the median vector and a useful hierarchical representation, facilitating a theoretical investigation of its properties. An additional advantage is its flexibility in modeling correlated response vectors without assuming the existence of the mean. The maximum likelihood approach is proposed to estimate the model parameters. Results are illustrated by applying the proposed methodology to the health data sets for investigating the risk factors associated with childhood obesity. © 2022 Elsevier Inc.
Publication Date: 2021
AStA Advances in Statistical Analysis (1863818X)105(2)pp. 197-228
Random-effects models are frequently used to analyze clustered binomial data. The direct computation of the marginal mean response, when integrated over the distribution of random effects, is challenging due to taking nonclosed-form expressions of the marginal link function. This paper extends the marginalized modeling methodology using innovative link functions, where the marginal mean response is modeled in terms of covariates and random effects. To derive the explicit closed-form representation of both marginal and conditional means, the regression structure is designed through an original strategy to introduce particular random-effects distributions. It will consequently allow for a reasonable interpretation of covariate effects. A Bayesian approach is employed to make the statistical inference by implementing the Markov chain Monte Carlo scheme. We conducted simulation studies to show the usefulness of our methodology. Two real-life data sets, taken from the teratology and respiratory studies, have been analyzed for illustration. The findings confirm that our new modeling methodology offers convenient settings for analyzing binomial responses in practice. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
Publication Date: 2020
Medical Journal Of The Islamic Republic Of Iran (22516840)34(1)
Background: Coriandrum sativum (coriander) is prescribed as a treatment for headache in traditional Persian medicine. Several investigations have been carried out to find the medicinal properties of this plant. However, no study has evaluated the effectiveness of this plant on becoming migraine-free. Methods: Sixty-eight migraineurs were randomly allocated to two equal groups of intervention and control. Each received 500 mg of sodium valproate in addition to 15 mL of coriander or placebo syrup three times a day. We followed subjects and recorded their migraine duration in the 1st, 2nd, 3rd, and 4th weeks. We applied an appropriate statistical model so as to consider special features of the data, which led to more accurate results using SAS 9.4 Results: Our findings showed that the probability of being migraine-free was not only considerably higher in final weeks of the study (p<0.001) in all patients of the intervention group than placebo group, but it was also significantly higher in patients less than 30 years of age compared to patients older than 30 years old. Migraine duration in migraineurs using coriander syrup reduced considerably during the study (p<0.001). Conclusion: The finding of this study revealed that coriander has a significant effect both on the probability of being migraine free and the duration of migraine attacks. Its effects were more significant during the final weeks of study. © Iran University of Medical Sciences.
Publication Date: 2020
Emerging Markets Finance and Trade (1540496X)56(13)pp. 3217-3234
This paper explores the nature of the causal relationship between market power and cost efficiency for a sample of banks operating in the Iranian banking industry over the period 2002–2014. In particular, a bootstrap panel Granger causality approach is used to test the “quiet life”, “banking specificities”, and “efficient-structure” hypotheses, accounting for both slope heterogeneity and cross-sectional dependence. The results indicate that, on average, banks’ market power (measured by the efficiency-adjusted Lerner index) has steadily declined over the study period, while cost efficiency (measured by the SFA approach) has improved over the period. Furthermore, the results of the causality analysis suggest that there is a negative unidirectional causality running from market power to cost efficiency for 56.25% of banks, providing evidence to support the “quiet life” hypothesis, according to which banks with greater market power would be less cost-efficient. Such a result clearly rejects the “banking specificities” and “efficient-structure” hypotheses which predict a positive Granger causality in the same and opposite directions, respectively. These results are robust to the use of an alternative Granger non-causality procedure. ©, Copyright © Taylor & Francis Group, LLC.
Publication Date: 2020
Diabetes, Metabolic Syndrome and Obesity (11787007)13pp. 1863-1872
Background: Increase in the prevalence of type 2 diabetic mellitus (T2DM) as a complex disease, its complications, and spread has become a dominant global health threat in recent decades. Objective: The aim of the current study was to investigate the impact of risk factors and transition probability on the development and progression of the late complications of T2DM. Methods: This study was an open cohort one which was conducted at Isfahan Endocrine and Metabolism Research Center (IEMRC). The data were collected from 1993 to 2018. The sample size consisted of 2519 adults diagnosed with type 2 diabetes. We applied the homogeneous multistate models including no complication, retinopathy alone, coronary artery disease (CAD), microalbuminuria, retinopathy and CAD, and the final absorbing mortality states. Results: Based on our results, time-varying hypertension strongly intensified the hazard of transition to mortality in CAD, no complication, CAD and retinopathy, and retinopathy patients by 4.99, 4.09, 3.42, and 2.65 times, respectively. Hypertension seemed to be a potential factor for the transition of microalbuminuria to no complication in diabetic patients. One-unit increase in LDL increased the hazard ratio of transition from CAD, and retinopathy and CAD to mortality by 1.8% and 2.4%, respectively. Moreover, one level increase in time-varying HbA1c increased the hazard ratio of transition to retinopathy and mortality among no complication diabetic patients by 30% and 67%, respectively. One level increase in time-varying HbA1c also intensified the hazard ratio of transition from retino-pathy to mortality by 45%. The same level of increase in time-varying HbA1c also intensified the hazard ratio of transition from CAD alone to CAD and retinopathy, and microalbuminuria to retinopathy by 26% and 50%, respectively. Conclusion: In addition to glycemic control, our study indicates that controlling hypertension and hyperlipidemia is more effective in reducing mortality and the diabetic macro-and microvascular complications. © 2020 Aliyari et al.
Publication Date: 2020
Journal Of Research In Medical Sciences (17357136)25(1)
Background: Prediabetes is strongly associated with high blood pressure; however, a little is known about prediabetes and high blood pressure comorbidity in the high-risk individuals. This is the first study in the world to assess the long-term effects of risk factors associated with high blood pressure and prediabetes comorbidity in the first-degree relatives (FDRs) of type 2 diabetes mellitus (T2DM) patients. Materials and Methods: The longitudinal data obtained from 1388 nondiabetic FDRs of T2DM patients with at least two visits between 2003 and 2011. We used univariate and bivariate mixed-effects logistic regressions with a Bayesian approach to identify longitudinal predictors of high blood pressure and prediabetes separately and simultaneously. Results: The baseline prevalence of high blood pressure, prediabetes, and the coexistence of both was 27.4%, 19.1%, and 29.8%, respectively. The risks of high blood pressure and prediabetes were increased by one-unit raise in the age (odds ratio [OR] of high blood pressure: 1.419 (95% credible intervals [CI], 1.077-1.877), prediabetes: 1.055 (95% CI: 1.040-1.068)) and one-unit raise in remnant-cholesterol (OR of high blood pressure: 1.093 (95%CI, 1.067-1.121), and prediabetes: 1.086 (95% CI, 1.043-1.119)). Obese participants were more likely to have high blood pressure (OR: 2.443 [95% CI, 1.978-3.031]) and prediabetes (OR: 1.399 [95% CI, 1.129-1.730]) than other participants. Conclusion: We have introduced remnant-cholesterol, along with obesity and age, as a significant predictor of prediabetes, high blood pressure, and the coexistence of both in the FDRs of diabetic patients. Obesity index and remnant-cholesterol showed the stronger effects on high blood pressure and prediabetes comorbidity than on each condition separately. © 2019 Wolters Kluwer Medknow Publications. All rights reserved.
Publication Date: 2020
Communications in Statistics Part B: Simulation and Computation (15324141)49(9)pp. 2419-2443
In this paper, we develop a new discrete bivariate distribution, i.e., the weighted bivariate geometric distribution whose marginals are univariate weighted geometric distributions and show that the proposed distribution is more flexible and applicable than the classical bivariate geometric distribution, which is in fact a special case of the proposed model, and the bivariate Freund distributions. We shall determine several important distributional and reliability characteristics of the distributions. Further, we shall develop classical and Bayesian inferences for the unknown parameters. Extensive Monte Carlo simulations and analysis of a real data set are conducted. © 2018 Taylor & Francis Group, LLC.
Publication Date: 2019
Journal of Multivariate Analysis (0047259X)174
This paper presents an attractive extension of multivariate mixed-effects models to allow the modeling of correlated responses. By initiating a new multivariate multimodal distribution, the proposed strategy takes multimodality and the asymmetric structure into account in a flexible way. It can also accommodate clustered random effects on multiple longitudinal responses when data comprise various hidden sub-populations that are not directly identifiable. We introduce an explicit stochastic hierarchical representation of the proposed model to render its theoretical properties straightforward and to carry out estimation processes easily. A fully Bayesian approach is proposed to compute posterior distributions using MCMC techniques in modeling multivariate longitudinal data. Moreover, we present an EM-based maximum likelihood estimation procedure. To facilitate Bayesian computation, the estimation process of mixed models utilizes a data augmentation scheme. We analyze two real-life data on the low-back pain study and the height of school-girls to illustrate the usefulness of our proposed model in practical applications. © 2019 Elsevier Inc.
Publication Date: 2019
Diabetes, Metabolic Syndrome and Obesity (11787007)12pp. 1123-1139
Background: Moderately increased albuminuria (MIA) is strongly associated with hypertension (HTN) in patients with type 2 diabetic mellitus (T2DM). However, the association between risk factors and coexisting HTN and MIA remains unassessed. Objectives: This study aimed to determine both cross-sectional and longitudinal associations of risk factors with HTN and MIA comorbidity in patients with T2DM. Methods: A total of 1,600 patients with T2DM were examined at baseline and longitudinal data were obtained from 1,337 T2DM patients with at least 2 follow-up visits to assess the presence of HTN alone (yes/no), MIA alone (yes/no) and the coexistence of both (yes/no) in a 9-year open cohort study between 2004 and 2013. Bivariate mixed-effects logistic regression with a Bayesian approach was employed to evaluate associations of risk factors with HTN and MIA comorbidity in the longitudinal assessment. Results: After adjustment for age and BMI, patients with uncontrolled plasma glucose, as a combined index of the glucose profile, were more likely to have HTN [odds ratio (OR): 1.73 with 95% Bayesian credible intervals (BCI) 1.29–2.20] and MIA [OR: 1.34 (95% BCI 1.13– 1.62)]. The risks of having HTN and MIA were increased by a one-year raise in diabetes duration [with 0.89 (95% BCI 0.84–0.96) and 0.81 (95% BCI 0.73–0.92) ORs, respectively] and a one-unit increase in non-high-density lipoprotein-cholesterol (Non-HDL-C) [with 1.30 (95% BCI 1.23–1.34) and 1.24 (95% BCI 1.14–1.33) ORs, respectively]. Conclusions: T2DM patients with HTN, MIA, and the coexistence of both had uncontrolled plasma glucose, significantly higher Non-HDL-C, and shorter diabetes duration than the other T2DM patients. Duration of diabetes and uncontrolled plasma glucose index showed the stronger effects on HTN and MIA comorbidity than on each condition separately. © 2019 Hadi Alijanvand et al.
Publication Date: 2019
Journal of Statistical Theory and Practice (15598616)13(3)
Copulas are the most powerful tools in constructing continuous multivariate distributions given marginals and certain dependence structures. In this paper, we shall propose a general and novel method of generating discrete bivariate distributions using copulas. The advantage of our method is that, contrary to the standard methods, we do not need to have the joint distribution of the base variables, but we only need the marginal distributions. In particular, we shall concentrate on generating a new family of discrete bivariate exponentiated extended Weibull (DBEEW) distribution by a Cuadras–Auge copula. We shall study several mathematical properties of a DBEEW distribution. Three special submodels of our new proposed distribution are studied in more detail. Finally, estimation of parameters for these three submodels is investigated and their potential and flexibility are examined using a real data set. © 2019, Grace Scientific Publishing.
Publication Date: 2019
Clinical Nutrition (0261-5614)38(3)pp. 1246-1252
Background & aims: The ability of nuts to improve the conditions of the metabolic syndrome (MetS) is now well established. However, few longitudinal studies examined the impact of nuts on MetS and those that have been ongoing considered baseline measurement of nuts intake. The associations between nuts intake and the risk of MetS was longitudinally assessed in our study using repeated measurements of nuts intake. Methods: The population-based longitudinal study was conducted on a sub-sample of the Isfahan Cohort Study (ICS), including 1387 adults, aged ≥ 35 years. A validated food frequency questionnaire was applied to obtain data on the nuts intake. International Diabetes Federation (IDF) criteria were used to define MetS. The longitudinal relation between the trend of nuts intake and the risk and severity of MetS was examined using the Logistic and Cumulative Logit regressions with considering mixed random effects. Results: After adjustment for potential confounders, a statistically significant inverse association was found in severity of MetS (the number of positive criteria) in the second quartile of nuts compared with the lowest quartile (OR: 0.77, 95% PI: 0.63–0.96; P trend: 0.03). Nuts intake was inversely associated with MetS risk among participants in the second quartile compared with the lowest quartile (OR: 0.76, 95% PI: 0.59–0.96; P trend: 0.14). Conclusions: Nuts intake demonstrated a significant, inverse association with the risk and severity of MetS after a 13-year follow-up period in a cohort of the Iranian population. © 2018 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism
Publication Date: 2018
Journal of Pediatric Endocrinology and Metabolism (0334018X)31(4)pp. 443-449
This study aimed to assess determinants of anthropometric measures in a nationally representative sample of Iranian children and adolescents. This nationwide study was conducted among 13,280 students, aged 6-18 years, who were randomly selected from 30 provinces in Iran. Anthropometric measures were determined by calibrated instruments. Demographic and socio-economic (SES) variables, lifestyle behaviors, family history of chronic disease and prenatal factors were studied, as well. A hierarchical Bayesian tri-variate analysis was used to assess the factors associated with obesity measures of the body mass index (BMI), waist-to-height ratio (WHtR) and wrist circumference (WrC). The results showed that the BMI was associated with SES score, family history of obesity, family history of diabetes mellitus, physical inactivity, screen time, duration of sleep, breakfast consumption, birth weight, breastfeeding, junk food and place of residence (urban-rural). All these factors were also significantly associated with WrC except for consumption of junk food. Many of these factors had a partial but significant relationship with WHtR. Various factors contribute to obesity. Preventive and educational programs on manageable factors such as increasing physical activity, eating breakfast and limiting TV or screen time could be helpful in controlling obesity in schoolchildren and reducing associated complications. © 2018 Walter de Gruyter GmbH, Berlin/Boston.
Publication Date: 2018
Brazilian Journal of Probability and Statistics (01030752)32(3)pp. 497-524
In this paper, we shall construct a large class of new bivariate copulas. This class happens to contain several known classes of copulas, such as Farlie–Gumbel–Morgenstern, Ali–Mikhail–Haq and Barnett–Gumbel, as its especial members. It is shown that the proposed copulas improve the range of values of correlation coefficient and thus they are more applicable in data modeling. We shall also reveal that the dependent properties of the base copula are preserved by the generated copula under certain conditions. Several members of the new class are introduced as instances and their range of correlation coefficients are computed. © Brazilian Statistical Association, 2018.
Publication Date: 2018
Nutrition, Metabolism and Cardiovascular Diseases (15903729)28(4)pp. 352-360
Background and aims: Diet is a potential factor contributing to the development of the Metabolic Syndrome (MetS). This longitudinal study with repeated measurements of dietary intake was thus conducted to examine the longitudinal association between major dietary patterns and risk of MetS. Methods and results: The study was conducted within the framework of the Isfahan Cohort Study (ICS), in which 1387 participants were followed from 2001 to 2013. Validated food frequency questionnaire, anthropometric measurements, blood pressure, fasting serum lipids and blood sugars were evaluated in three phases of the study. Mixed effect Logistic and Cumulative Logit regressions were applied to evaluate the longitudinal associations between dietary patterns change and MetS and number of MetS components. Three dietary patterns were identified: Healthy, Iranian and Western dietary patterns. After adjustment for potential confounders, the higher scores of Healthy diet were inversely associated with the risk of MetS and number of MetS components (OR: 0.50, 95% CI: 0.36–0.70, OR: 0.52, 95% CI: 0.39–0.70, respectively). The greater adherence to the Iranian diet was positively associated with the risk of MetS and number of MetS components (OR: 1.28, 95% CI: 1.01–1.65, OR: 1.45, 95% CI: 1.16–1.81, respectively). The Western dietary pattern did not show any significant associations. Conclusion: Adherence to a Healthy diet was associated with lower risk of MetS even in a developing country setting. However, the Iranian diet was positively associated with the risk of MetS. These results may guide the development of improved preventive nutrition interventions in this adult population. © 2017
Publication Date: 2017
Journal of Statistical Computation and Simulation (15635163)87(1)pp. 171-186
In this paper, we investigate estimation methods to deal with situations where random intercepts are associated to time-varying covariates in the context of linear mixed models. First, a review of previous ways to deal with this so-called endogeneity issue is presented, then a new method based on shared random effects is proposed. Simulation studies and an empirical example are utilized to assess the performance of our proposed method. It is shown that our new approach is more efficient than most competitors and is robust to the misspecification of the random-effects distributions. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2017
Iranian Red Crescent Medical Journal (20741804)19(10)
Background: The change and trend in serum albumin levels after initiation of peritoneal dialysis may be a crucial determinant for clinical outcomes. Objectives: The current study aimed at determining the association between the trajectory of serum albumin and the 3-year clinical outcomes using a novel approach joint modeling longitudinal and survival data. Furthermore, the current study was performed to compare the impact of baseline and trajectory serum albumin on predictability of 3-year outcomes. Methods: The current retrospective longitudinal study reviewed all of the available files of the patients undergoing continuous ambulatory peritoneal dialysis (CAPD) in Al-Zahra hospital, Isfahan, Iran, from May 2005 to March 2015. A total of 183 patients with at least 3 years follow-up were selected for the study. The independent variables of interest were baseline and the trajectories of serum albumin, age, gender, history of previous hemodialysis (HD), body mass index (BMI), baseline serum creatinine, and comorbidity including cardiovascular disease and diabetes. The outcomes of interest were death from all causes, transfer to HD and transplantation during the first 3 years of CAPD. Results: The patient and technique survival rates at 36 months were 71% and 77%, respectively. C-indexes (prediction errors) of mortality, transfer to HD, and transplantation for joint modeling with trajectories of serum albumin were higher (lower) than those of the Cox regression with baseline albumin. Hazard ratios of mortality, transfer to HD, and transplantation for trajectories of serum albumin were 0.409, 0.273, and 3.394, respectively. Conclusions: The current study indicated that the predictability of 3-year clinical outcomes using trajectories of serum albumin was higher than those of the baseline. According to the findings of the current study, it seems that controlling serum albumin over time in patients undergoing CAPD, particularly the ones with the history of diabetes and HD, can help to prevent or modify the clinical outcomes during the PD period. © 2017, Iranian Red Crescent Medical Journal.
Publication Date: 2017
Statistical Papers (09325026)58(3)pp. 791-809
This paper extends regression modeling of positive count data to deal with excessive proportion of one counts. In particular, we propose one-inflated positive (OIP) regression models and present some of their properties. Also, the stochastic hierarchical representation of one-inflated positive poisson and negative binomial regression models are achieved. It is illustrated that the standard OIP model may be inadequate in the presence of one inflation and the lack of independence. Thus, to take into account the inherent correlation of responses, a class of two-level OIP regression models with subjects heterogeneity effects is introduced. A simulation study is conducted to highlight theoretical aspects. Results show that when one-inflation or over-dispersion in the data generating process is ignored, parameter estimates are inefficient and statistically reliable findings are missed. Finally, we analyze a real data set taken from a length of hospital stay study to illustrate the usefulness of our proposed models. © 2015, Springer-Verlag Berlin Heidelberg.
Publication Date: 2017
Kidney Research and Clinical Practice (22119132)36(2)pp. 182-191
Background: In peritoneal dialysis, technique failure is an important metric to be considered. This study was performed in order to identify the relationship between trajectories of serum albumin levels and peritoneal dialysis technique failure on end-stage renal disease patients according to diabetic status. Furthermore, this study was performed to reveal predictors of serum albumin and technique failure simultaneously. Methods: This retrospective cohort study included 300 (189 non-diabetic and 111 diabetic) end-stage renal disease patients on continuous ambulatory peritoneal dialysis treated in Al-Zahra Hospital, Isfahan, Iran, from May 2005 to March 2015. Bayesian joint modeling was carried out in order to determine the relationship between trajectories of serum albumin levels and peritoneal dialysis technique failure in the patients according to diabetic status. Death from all causes was considered as a competing risk. Results: Using joint modeling approach, a relationship between trajectories of serum albumin with hazard of transfer to hemodialysis was estimated as -0.720 (95% confidence interval [CI], -0.971 to -0.472) for diabetic and -0.784 (95% CI, -0.963 to -0.587) for non-diabetic patients. From our findings it was showed that predictors of low serum albumin over time were time on peritoneal dialysis for diabetic patients and increase in age and time on peritoneal dialysis, history of previous hemodialysis, and lower body mass index in non-diabetic patients. Conclusion: The results of current study showed that controlling serum albumin over time in non-diabetic and diabetic patients undergoing continuous ambulatory peritoneal dialysis treatment can decrease risk of adverse outcomes during the peritoneal dialysis period. © 2017 by The Korean Society of Nephrology.
Publication Date: 2016
Journal of Statistical Computation and Simulation (15635163)86(13)pp. 2644-2662
Mixed Poisson distributions are widely used in various applications of count data mainly when extra variation is present. This paper introduces an extension in terms of a mixed strategy to jointly deal with extra-Poisson variation and zero-inflated counts. In particular, we propose the Poisson log-skew-normal distribution which utilizes the log-skew-normal as a mixing prior and present its main properties. This is directly done through additional hierarchy level to the lognormal prior and includes the Poisson lognormal distribution as its special case. Two numerical methods are developed for the evaluation of associated likelihoods based on the Gauss–Hermite quadrature and the Lambert's W function. By conducting simulation studies, we show that the proposed distribution performs better than several commonly used distributions that allow for over-dispersion or zero inflation. The usefulness of the proposed distribution in empirical work is highlighted by the analysis of a real data set taken from health economics contexts. © 2015 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2016
Advances in Data Analysis and Classification (18625347)10(4)pp. 541-562
In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster memberships. The method uses the Dirichlet-t distribution which notably utilizes the robustness feature of the student-t distribution in the framework of a Bayesian semi-parametric approach together with robust clustering of subjects evaluates the uncertainty level of subjects memberships to their clusters. We let the number of clusters and the uncertainty levels be unknown while fitting Dirichlet process mixture models. Two simulation studies are conducted to demonstrate the proposed methodology. The method is applied to cluster a real data set taken from gene expression studies. © 2016, Springer-Verlag Berlin Heidelberg.
Publication Date: 2014
Statistics in Medicine (02776715)33(27)pp. 4743-4755
In this paper, we investigate the impact of time-invariant covariates when fitting transition mixed models. This is carried out by emphasizing on the role of baseline responses on the estimation process. Transition models are allowed for two cases of exogenous and endogenous baseline responses. We illustrate these concepts in the special case of transition linear mixed models with centered time-varying covariates. Results of our simulation studies show that the omission, or the inclusion, of time-invariant covariates is not important in models with exogenous baseline responses, while it has an essential effect on fitting models with the endogenous baseline responses. It is also emphasized that the effect becomes minor when the endogeneity issue is handled. The practical consequences are illustrated in the analysis of a real data set taken from medical sciences. © 2014 John Wiley & Sons, Ltd.
Publication Date: 2014
Journal of Tropical Pediatrics (14653664)60(1)pp. 61-67
Cell blood counts are components of hematological parameters and indicators of pro-inflammatory states. They are proposed to be associated with metabolic syndrome (MetS). This study aimed to assess the relationship of the white blood cell (WBC) and the red blood cell (RBC) counts with components of MetS in the pediatric age group. The sample consisted of 300 children (152 boys) aged 6-12 years. Hierarchical Bayesian analysis of the bivariate Poisson regression model was used to estimate the effect of various components of MetS according to the cell blood counts. We found that RBC and WBC counts were correlated with the fasting blood glucose, the waist-to-height ratio, serum triglycerides and the blood pressure levels adjusted for age, the body mass index, gender, total cholesterol, low-density lipoprotein cholesterol and the hip circumference. The high-density lipoprotein cholesterol was correlated with the RBC counts based on 95% high posterior density regions for parameters in the Bayesian model. Our findings may serve as confirmatory evidence for the beginning of inflammatory process related to the cardio-metabolic factors from early life. © The Author [2013]. Published by Oxford University Press. All rights reserved.
Publication Date: 2014
Communications in Statistics - Theory and Methods (1532415X)43(8)pp. 1630-1648
A common assumption in fitting panel data models is normality of stochastic subject effects. This can be extremely restrictive, making vague most potential features of true distributions. The objective of this article is to propose a modeling strategy, from a semi-parametric Bayesian perspective, to specify a flexible distribution for the random effects in dynamic panel data models. This is addressed here by assuming the Dirichlet process mixture model to introduce Dirichlet process prior for the random-effects distribution. We address the role of initial conditions in dynamic processes, emphasizing on joint modeling of start-up and subsequent responses. We adopt Gibbs sampling techniques to approximate posterior estimates. These important topics are illustrated by a simulation study and also by testing hypothetical models in two empirical contexts drawn from economic studies. We use modified versions of information criteria to compare the fitted models. © 2014 Taylor & Francis Group, LLC.
Publication Date: 2013
Biometrical Journal (03233847)55(4)pp. 495-508
Classical multivariate mixed models that acknowledge the correlation of patients through the incorporation of normal error terms are widely used in cohort studies. Violation of the normality assumption can make the statistical inference vague. In this paper, we propose a Bayesian parametric approach by relaxing this assumption and substituting some flexible distributions in fitting multivariate mixed models. This strategy allows for the skewness and the heavy tails of error-term distributions and thus makes inferences robust to the violation. This approach uses flexible skew-elliptical distributions, including skewed, fat, or thin-tailed distributions, and imposes the normal model as a special case. We use real data obtained from a prospective cohort study on the low back pain to illustrate the usefulness of our proposed approach. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Publication Date: 2013
International Journal of Environmental Health Engineering (22779183)2(2)
Aims: The aim of this study was to investigate the relationship between longitudinal change in total cholesterol as a main cardiovascular disease risk factor and shift work, controlling for the effect of the weight at baseline of recruitment. Materials and Methods: This retrospective cohort study consists of 674 employees of Iranian Corporation Polyacril from 1992 to 2009. Stratified analysis of the relationship between shift work and cholesterol based on weight status at baseline of recruitment controlled for the effect of confounders including age, body mass index, pre-employment cholesterol, glucose, triglyceride, urea, work types, education, and marital status. A linear mixed model used for analyzing the data. Estimation of parameters has done by Bayesian approaches using Winbugs statistical software. Bayesian confidence interval (CI) was used for testing regression coefficients. Results: Average age mean at employment was 25 years (standard deviation [SD] =3.3); the average number of measurement for each individual was 3.7 times (SD = 0.6). In this model, relationship between shift work and cholesterol changes controlled for confounding factors was significant in whom overweight was at baseline (beta = 2.25, P < 0.001, 95% CI: 0.67-3.88) but was not significant for whom overweight was at baseline of employment. Conclusions: The rate of cholesterol changes was higher for normal weight shift workers compared with workers who were overweight at baseline of recruitment. Copyright: © 2013 Salehi-Marzijarani M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2013
International Journal of Environmental Health Engineering (22779183)2(2)
Aims: Traditionally, roadway safety analyses have used univariate distributions to model crash data for each level of severity separately. This paper uses the multivariate Poisson lognormal (MVPLN) models to estimate the expected crash frequency by two levels of severity and then compares those estimates with the univariate Poisson-lognormal (UVPLN) and the univariate Poisson (UVP) models. Materials and Methods: The parameters estimation is done by Bayesian method for crash data at two levels of severity at the intersection of Isfahan city for 6 months. Results: The results showed that there was over-dispersion issue in data. The UVP model is not able to overcome this problem while the MVPLN model can account for over-dispersion. Also, the estimates of the extra Poisson variation parameters in the MVPLN model were smaller than the UVPLN model that causes improvement in the precision of the MNPLN model. Hence, the MVPLN model is better fitted to the data set. Also, results showed effect of the total Average annual daily traffic (AADT) on the property damage only crash was significant in the all of models but effect of the total left turn AADT on the injuries and fatalities crash was significant just in the UVP model. Hence, holding all other factors fixed more property damage only crashes were expected on more the total AADT. For example, under MVPLN model an increase of 1000 vehicles in (average) the total AADT was predicted to result in 31% more property damage only crash. Conclusion: Hence, reduction of total AADT was predicted to be highly cost-effective, in terms of the crash cost reductions over the long run. Copyright: © 2013 Tazhibi M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2013
Computational Statistics (16139658)28(5)pp. 2007-2027
In this paper, we develop a semi-parametric Bayesian estimation approach through the Dirichlet process (DP) mixture in fitting linear mixed models. The random-effects distribution is specified by introducing a multivariate skew-normal distribution as base for the Dirichlet process. The proposed approach efficiently deals with modeling issues in a wide range of non-normally distributed random effects. We adopt Gibbs sampling techniques to achieve the parameter estimates. A small simulation study is conducted to show that the proposed DP prior is better at the prediction of random effects. Two real data sets are analyzed and tested by several hypothetical models to illustrate the usefulness of the proposed approach. © 2013 Springer-Verlag Berlin Heidelberg.
Publication Date: 2012
Iran Occupational Health (17355133)9(3)pp. 96-104
Background and aims: 20TManagers decision making style can function effectively correct departments in universities and its positive impact on organizational health group will increase efficiency. The present study aims to examine the relationship between the decision-making styles and organizational health departments in Isfahan University of Medical Sciences. 20TMethods: 20TA descriptive and survey research method was utilized. The statistical population included20T all 594 members 20Tof Isfahan Medical Science University Colleges from which a sample of 201 was selected though a classified random sampling The data gathering instruments included, a researcher made decision20T making questionnaire20T and the Ho & Feldmn (1990), 20Torganizational20T health questionnaire. The reliability of the instruments was estimated 0.86 and 0.92 respectively, though Cronbach Alpha coefficient. Utilizing SPSS (15) statistical software, both descriptive and inferential statistics were applied to analyze the data. 20TResults:20T Consultative decision making scored the highest average among the chairpersons, while the authoritative style scored the lowest average. The departments' organizational health was more than mean level in all dimensions except chairperson's influence. Moreover, a significant relationship was observed between decision making style and organizational health indices. Also a direct relationship was not observed between authoritarian decision makings and institutional integration, chairperson influence, consideration, initiating structure, and academic emphasis. A direct relationship was observed between Consultative decision making and chairperson influence, consideration, initiating structure, resource support, morale, and academic emphasis. A direct relationship was observed between Participative decision making and chairperson Influence, consideration, initiating structure. Conclusion: Consultative and participative decision making can lead to enhancement and maintenance of organizational health. High morale and institutional integration are among important organizational health dimensions and they enhance teaching and research processes in departments.
Publication Date: 2012
Journal of Applied Statistics (02664763)39(5)pp. 1087-1100
In the analysis of correlated ordered data, mixed-effect models are frequently used to control the subject heterogeneity effects. A common assumption in fitting these models is the normality of random effects. In many cases, this is unrealistic, making the estimation results unreliable. This paper considers several flexible models for random effects and investigates their properties in the model fitting. We adopt a proportional odds logistic regression model and incorporate the skewed version of the normal, Student's t and slash distributions for the effects. Stochastic representations for various flexible distributions are proposed afterwards based on the mixing strategy approach. This reduces the computational burden being performed by the McMC technique. Furthermore, this paper addresses the identifiability restrictions and suggests a procedure to handle this issue. We analyze a real data set taken from an ophthalmic clinical trial. Model selection is performed by suitable Bayesian model selection criteria. © 2012 Copyright Taylor and Francis Group, LLC.
Publication Date: 2012
International Education Studies (discontinued) (19139020)5(2)pp. 175-184
The purpose of this study was to provide an exploratory investigation of faculty member's efficacy inventory in higher education. Review of the literature showed a few studies about this subject and current instruments did not consider the theoritical foundations of faculty member efficacy. Moreover, most researches were limited to schools area and K-12. After an extensive review of the literature, first, a set of items to operationalize faculty perceptions and beliefs of efficacy in their tasks was developed. At second stage, higher education colleagues who were working in our university and other nearby universities examined the items for critique, and consulted with their colleagues about content and face validity. Third, a pilot study was initiated to map the domain of the construct and refined the measure and the meaning of faculty efficacy through the statistical methods. The instrument was field-tested and refined using a representative sample of universities faculty. Fourth, a factor analysis was utilized to identify factors related to efficacy scale of faculty members. Fifth, we reduced items and agreed about 18. Four factors were appeared in the factor analysis consisting of teaching competencies, research competencies, social competencies, and personal competencies. We insured all four sources of efficacy (mastery experiences, vicarious experiences, social persuasion, and emotional arousal) were represented in each efficacy components (teaching competencies, research competencies, social competencies and personal competencies). Cronbach's alpha coefficient was calculated for each factor and in overall the instrument was a reliable scale 0.83. Finally, differences between faculty members were studied based on some demographic variables such as gender and academic ranking. Results showed that there were not significant differences between all female and male faculty members efficacy and so based on academic ranking.
Publication Date: 2012
Statistical Modelling (14770342)12(6)pp. 503-525
In this paper, we consider the analysis of unequally spaced longitudinal data using transition regression models with random effects. Diffusion as well as stabilization processes will be discussed, but our main focus will be on the latter. The initial conditions problem, which usually arises in transition models with random effects, is addressed. The usefulness of the proposed model is assessed on a large database of longitudinal haemoglobin values collected from blood donations by a Dutch private organization. © 2012 SAGE Publications.
Publication Date: 2011
Journal of Isfahan Medical School (10277595)29(125)
Background: The metabolic syndrome is characterized by a group of metabolic risk factors at increased risk of coronary heart disease and type 2 diabetes. Obese children with metabolic syndrome have at least three of these risk factors. Since the lifestyle changed resulting in the obesity of Iranian children, this syndrome became one of the important concerns in these age groups. There have been many researches on the relationship between this syndrome and number of blood cells in children. However, it was not considered in Iran to some extent. Since the number of blood cells is a counting variable, using traditional statistical methods ends up with erroneous inferences. In this paper, the generalized Poisson model was used to identify the effect of metabolic syndrome on the number of (red and white) blood cells. Methods: 292 obese children in the age group of 6 to 12 years old participated in the children Hospital of Isfahan Medical University for this cross-sectional Study. Metabolic syndrome characteristics were analyzed considering coronary and growth parameters. Finding: Generalized Poisson model, as the best fitting model on the count data, showed that the following two factors, BMI and ratio between triglyceride and HDL-C, significantly affect the number of white blood cells; the ratio between cholesterol and HDL-C, the ratio between triglyceride and HDL-C and the ratio between LDL-C and HDL-C had a significant effect on the number of red blood cells. Conclusion: This study shows that some of the characteristics of metabolic syndrome affect the number of blood cells.
Publication Date: 2011
Journal of Isfahan Medical School (10277595)29(127)
Background: Diabetic macular edema is one of the most prevalent outcomes between diabetic patients. According to high prevalence of this disorder and its related outcomes between diabetic patients, the goal of this study is determining the efficacy of a single intravitreal injection of bevacizumab (IVB) alone or in combination with triamcinolone versus macular laser photocoagulation (MPC) as primary treatment for diabetic macular edema (DME) using updated statistical modelling. Methods: This modeling was performed on 102 diabetic patients in three above-mentioned groups who had diabetic macular edema and did not get any treatment before the beginning of clinical trials. A mixed model with non-normal random effects has been proposed to compare the effects of treatment on diabetic macular edema by omitting the effects of confounders. Effect of these treatments has been investigated according to variation of best-corrected Visual Acuity and central macular thickness, as two major outcomes. Finding: The results of mixed modelling with non-normal random effects according to omitting the effects of confounders in the period of study, show that the Intravitreal Bevacizumab injection alone or combined with Triamcinolone treatments have more Therapeutic effect than the Macular Photocoagulation treatment on Diabetic Macular Edema patients. Conclusion: According to this investigation, patients with more diabetic duration who had more central macular tickness and less best-corrected visual acuity in the beginning of the study had better response to interventions. More investigations showed using different treatments lead to different results on diabetic macular edema.
Publication Date: 2011
Journal Of Research In Medical Sciences (17357136)16(10)pp. 1319-1325
BACKGROUND: Diabetic Macular Edema (DME) is one of the major causes of visual loss and increase in central macular thickness (CMT). The aim of this study was to determine the efficacy of a single intravitreal injection of bevacizumab (IVB) alone or in combination with intravitreal triamcinolone acetonide (IVB/IVT) versus macular laser photocoagulation (MPC) as primary treatment for DME when confounders were considered. METHODS: Skew-symmetric bivariate mixed modeling according to best corrected visual acuity (BCVA) and CMT was done on the data of 103 diabetic patients from ophthalmic research center of Labbafinejad medical center (Tehran, Iran) to determine the best DME treatment by adjusting the effect of confounders. RESULTS: Although there was no significant difference between IVB/IVT (p > 0.05), these two treatments increased BCVA and decreased CMT better than MPC (p < 0.05). The following three groups showed better treatment responses: 1) women, 2) patients with more diabetes duration, 3) patients whose CMT were higher and VA were lower at the beginning of the clinical trial. CONCLUSIONS: Using skew-symmetric mixed effect model as updated statistical method in presence of asymmetric or outlier data, we received different results compared to the same investigation on this study by analyzing BCVA and CMT simultaneously. This research demonstrated the effect of IVB alone or in combination with intravitreal IVB/IVT on visual power and decreasing CMT during follow up.
Publication Date: 2010
Applied Mathematical Sciences (discontinued) (1312885X)4(61-64)pp. 3067-3082
In this paper some models are applied to analyze insomnia data. Insomnia is a sleep disorder in which the patient does not get enough or satisfactory sleep and investigating the use of hypnotic drug for its cure is so important. For studying the effect of drugs on insomnia, it is useful to observe the response of interest (cured or not cured) for each subject repeatedly at several times. So, a longitudinal study of repeated binary responses along with a treatment variable (with two levels hypnotic or placebo) are used for some individuals on two times. A very important feature of the data is that the correlation between the two longitudinal responses depends on the level of hypnotic drug. This is, firstly, shown by an exploratory data analysis. Then, as an option, Dale's bivariate model for analyzing longitudinal or repeated measurements of binary responses, considering the effect of treatment on the responses and the correlation structure, is used to find an overall population effect on the responses. As other options, transition and random effect models for analyzing these data are used to investigate, respectively, the reasons for the change of the responses and to find the subject-specific variations. How the interpretation of the results is different with the use of the Dale's model and how one may find out the effect of the drug on the correlation structure by a transition or random effect model are also discussed.
