Analysis of mixed correlated overdispersed binomial and ordinal longitudinal responses: LogLindley-Binomial and ordinal random effects model.

We propose a new model called LogLindley-Binomial and ordinal joint model with random effects for analyzing mixed overdispersed binomial and ordinal longitudinal responses. A new distribution called the LogLindley-Binomial is presented, which is appropriate for the analysis of overdispersed binomial variables. A full likelihood-based approach is used to obtain maximum likelihood estimates. A comparison between LogLindley-Binomial and Beta-Binomial distributions are given by a simulation study. Also, to illustrate the utility of the proposed model, some simulation studies are conducted. In simulation studies, the performances of the LogLindley-Binomial distribution and the proposed model are well in some situations. Also, the new model's performance for analyzing a real dataset, extracted from the British Household Panel Survey, is studied. The proposed model performs well in comparison with another model for analyzing real data. Finally, the proposed distribution and the new model are found to be applicable for analyzing overdispersed binomial and mixed data.

Joint model for longitudinal mixture of normal and zero-inflated power series correlated responses Abbreviated title:mixture of normal and zero-inflated power series random-effects model.

In this paper, a joint model is presented for analyzing longitudinal continuous and count mixed responses. The frequency distribution of continuous longitudinal response variable for each subject at any time has a skewed and or multi-modal form. Then, a suitable finite mixture of normals is used as its distribution. It seems that the continuous response comes from several distinct sub-populations. The number of zeros of the count response is inflated. Also, a zero-inflated power series (ZIPS) distribution is applied as its distribution in order to model the count response. The correlation of longitudinal responses through time and that of mixed continuous and count responses are modeled by utilizing the random-effects vectors in the finite mixtures of regression (FMR) models. Further, a full likelihood-based approach is used to obtain the maximum likelihood estimates of parameters via the EM algorithm. Then, some simulation studies are performed for assessing the performance of the model. Additionally, an application is illustrated for joint analysis of the number of days during the last month that the individual drank alcohol, as well as the respondents' weight. Finally, the two first times of the Americans Changing Lives survey are evaluated.

General location multivariate latent variable models for mixed correlated bounded continuous, ordinal, and nominal responses with non-ignorable missing data.

Using a multivariate latent variable approach, this article proposes some new general models to analyze the correlated bounded continuous and categorical (nominal or/and ordinal) responses with and without non-ignorable missing values. First, we discuss regression methods for jointly analyzing continuous, nominal, and ordinal responses that we motivated by analyzing data from studies of toxicity development. Second, using the beta and Dirichlet distributions, we extend the models so that some bounded continuous responses are replaced for continuous responses. The joint distribution of the bounded continuous, nominal and ordinal variables is decomposed into a marginal multinomial distribution for the nominal variable and a conditional multivariate joint distribution for the bounded continuous and ordinal variables given the nominal variable. We estimate the regression parameters under the new general location models using the maximum-likelihood method. Sensitivity analysis is also performed to study the influence of small perturbations of the parameters of the missing mechanisms of the model on the maximal normal curvature. The proposed models are applied to two data sets: BMI, Steatosis and Osteoporosis data and Tehran household expenditure budgets.

Reliability of a soccer player based on the bivariate Rayleigh distribution with right censored and ignorable missing data.

In this paper, we study the performance of a soccer player based on analysing an incomplete data set. To achieve this aim, we fit the bivariate Rayleigh distribution to the soccer dataset by the maximum likelihood method. In this way, the missing data and right censoring problems, that usually happen in such studies, are considered. Our aim is to inference about the performance of a soccer player by considering the stress and strength components. The first goal of the player of interest in a match is assumed as the stress component and the second goal of the match is assumed as the strength component. We propose some methods to overcome incomplete data problem and we use these methods to inference about the performance of a soccer player.

Assessing the Effects of Important Factors and Province Heterogeneity on Different Quantiles of Hospitalization Cost.

Objectives: The aim of the study was to investigate the effects of some covariates on different quantiles of the cost of hospitalization. The effect of the province that the individual belongs to on these quantiles will be also examined.Methods: We employed a linear quantile-mixed model (LQMM) for analyzing the cost of hospitalization in Iranians Utilization of Health Services (IUHS) survey considering the province effect, the effects of some important covariates, and also the effect of the choice of the random-effects distribution. For this, both classical and Bayesian approaches are used for parameter estimation.Results: The results of data analysis show that ward, type of hospital, and duration of hospitalization are significant factors on quantiles of the cost of hospitalization, of course with different impacts on different quantiles. Our findings reveal significant discrepancies in the cost of hospitalization in different provinces and significant heterogeneity among provinces.Conclusion: More works must be done related to hospitalization cost and its consequences since it is a matter of social life. To be exact, one should notice that provinces with hospitals involving high hospitalization costs may have households dealing with poverty.

Pair copula construction for longitudinal data with zero-inflated power series marginal distributions.

Assessing the temporal dependency among outcomes under investigation is critical in many fields. One complication in the modeling process of the discrete longitudinal data is the presence of excess zeros. We propose a framework for modeling count repeated measurements using members of power series family of distributions. The framework accommodates count outcomes having extra zeros. The longitudinal observations of response variable is modeled using pair copula constructions with a D-vine structure. The maximum likelihood estimates of parameters are obtained using a two-stage approach. Some simulation studies are performed for illustration of the proposed methods, for comparing its performance with that of a generalized linear mixed effects (GLME) model and for assessing the robustness of D-vine and GLME models with respect to the distribution of random effects. In the empirical analysis, the proposed method is applied for analysing a real data set of a kidney allograft rejection study.

Analysis of joint modeling of longitudinal zero-inflated power series and zero-inflated time to event data.

In longitudinal studies measurements are often collected on different types of responses for each individual. These may contain several longitudinally measured responses (such as the CD4 count) and the time at which an event occurs (e.g., HIV, death, or dropout from the study). These outcomes are often separately analyzed. Compared to separate modeling, joint modeling and simultaneous analysis allows for more coherent, robust analysis and may produce a better insight into the process under study. However, there has always been difficulty to the analyst that finding a proper multi-variable joint distribution for linking responses. In this article, we survey the zero-inflated property for longitudinal count and time to event data. We apply a member of the family of power series distributions (PSDs) and the Cox proportional hazard regression model (Cox PH) with Weibull baseline hazard rate, respectively, for these correlated responses. Also we consider both right and left censoring mechanisms in time to event process. This modeling strategy leads to expand the class of joint models and presents some new joint models which, as far as we know, have not yet been investigated by other researchers. The parameters in the joint model are estimated by using likelihood techniques.

Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study.

In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

Using bayesian model to estimate the cost of traffic injuries in Iran in 2013.

BACKGROUND AND AIM: A significant social and economic burden inflicts by road traffic injuries (RTIs). We aimed to use Bayesian model, to present the precise method, and to estimate the cost of RTIs in Iran in 2013. MATERIALS AND METHODS: In a cross-sectional study on costs resulting from traffic injuries, 846 people per road user were randomly selected and investigated during 3 months (1st September-1st December) in 2013. The research questionnaire was prepared based on the standard for willingness to pay (WTP) method considering perceived risks, especially in Iran. Data were collected along with four scenarios for occupants, pedestrians, vehicle drivers, and motorcyclists. Inclusion criterion was having at least high school education and being in the age range of 18-65 years old; risk perception was an important factor to the study and measured by visual tool. Samples who did not have risk perception were excluded from the study. Main outcome measure was cost estimation of traffic injuries using WTP method. RESULTS: Mean WTP was 2,612,050 internal rate of return (IRR) among these road users. Statistical value of life was estimated according to 20,408 death cases 402,314,106,073,648 IRR, equivalent to 13,410,470,202$ based on the dollar free market rate of 30,000 IRR (purchase power parity). In sum, injury and death cases came to 1,171,450,232,238,648 IRR equivalents to 39,048,341,074$. Moreover, in 2013, costs of traffic accident constituted 6.46% of gross national income, which was 604,300,000,000$. WTP had a significant relationship with age, middle and high income, daily payment to injury reduction, more payment to time reduction, trip mileage, private cars drivers, bus, minibus vehicles, and occupants (P < 0.01). CONCLUSION: Costs of traffic injuries included noticeable portion of gross national income. If policy-making and resource allocation are made based on the scientific pieces of evidence, an enormous amount of capital can be saved through reducing death and injury rates.

Cost Estimation of Road Traffic Injuries Among Iranian Motorcyclists Using the Willingness to Pay Method.

BACKGROUND: Motorcycle riders are amongst some of the most vulnerable road users. The burden of motorcycles injuries from low and middle income countries is under-reported. OBJECTIVES: In this study, the cost of traffic injuries among motorcyclists was calculated using the willingness to pay (WTP) method in Iran in 2013. PATIENTS AND METHODS: In a cross-sectional study, 143 motorcyclists were randomly selected. The research questionnaire was prepared based on the standard WTP method [stated preference (SP), contingent value (CV) and revealed preference (RP) models] taking into consideration perceived risks, especially those in Iran. Data were collected by a scenario for motorcyclists. The criteria for inclusion in the study consisted of having at least a high school education and being in the age range of 18 - 65 years. The final analysis of the WTP data was performed using the Weibull model. RESULTS: The mean WTP was 888,110 IRR (Iranian Rial) among motorcyclists. The statistical value of life was estimated according to 4694 death cases as 3,146,225,350,943 IRR, which was equivalent to USD 104,874,178 based on the dollar free market rate of 30,000 IRR (purchasing power parity). The cost of injury was 6,903,839,551,000 IRR, equivalent to USD 230,127,985 (based upon 73,325 injured motorcyclists in 2013, a daily traffic volume of 311, and a daily payment of 12,110 IRR for 250 working days). In total, injury and death cases came to 10,050,094,901,943 IRR, equivalent to USD 335,003,163. Willingness to pay had a significant relationship with having experienced an accident, the length of the daily trip (in km), and helmet use (P < 0.001). CONCLUSIONS: Willingness to pay can be affected by experiencing an accident, the distance of the daily trip, and helmet use. The cost of traffic injuries among motorcyclists shows that this rate is much higher than the global average. Thus, expenditure should be made on effective initiatives such as the safety of motorcyclists.

The Effect of Smear Layer on Antimicrobial Efficacy of Three Root Canal Irrigants.

INTRODUCTION: One of the main goals of endodontic treatment is to decrease the harboring bacteria within the root canal system and dentinal tubules. This experimental study attempted to investigate the antibacterial efficacy of three root canal irrigants in the presence and absence of smear layer (SL). METHODS AND MATERIALS: A total of 210 sound extracted human single-rooted teeth were prepared. After creating the SL and its removal in half of the samples, they were infected with Candida albicans (C. albicans) and Actinomyces israelii (A. israelii). A total of 180 specimen were used to assess the antimicrobial efficacy of the three irrigants in presence and absence of SL, 24 specimen were placed in the positive and negative controls, 2 samples were utilized for scanning electron microscopy (SEM) analysis and 2 were used for Gram staining. Then, they were exposed to irrigants including 2.61% sodium hypochlorite (NaOCl), 0.2% chlorhexidine gluconate (CHX) and 1% povidone-iodine (PI) for 5, 30 and 60 min. Presence/absence of test microorganisms was determined by incubation of specimens in test tubes containing brain-heart infusion (BHI) broth and then measuring the colony forming units (CFU) on BHI agar. A cumulative logistic model was used to analyze the ordinal response. RESULTS: The 2.61% solution of NaOCl was significantly more effective than 0.2% CHX and the latter was more efficient than 1% PI for decreasing fungal and microbial infection of dentinal tubules in the presence and absence of SL. CONCLUSION: The presence of smear layer decreased the efficacy of antimicrobial irrigants. The minimum time required for elimination of fungal/microbial infection was 30 min.

Eliciting road traffic injuries cost among Iranian drivers' public vehicles using willingness to pay method.

BACKGROUND AND AIM: To allocate resources at the national level and ensure the safety level of roads with the aim of economic efficiency, cost calculation can help determine the size of the problem and demonstrate the economic benefits resulting from preventing such injuries. This study was carried out to elicit the cost of traffic injuries among Iranian drivers of public vehicles. MATERIALS AND METHODS: In a cross-sectional study, 410 drivers of public vehicles were randomly selected from all the drivers in city of Tehran, Iran. The research questionnaire was prepared based on the standard for willingness to pay (WTP) method (stated preference (SP), contingent value (CV), and revealed preference (RP) model). Data were collected along with a scenario for vehicle drivers. Inclusion criteria were having at least high school education and being in the age range of 18 to 65 years old. Final analysis of willingness to pay was carried out using Weibull model. RESULTS: Mean WTP was 3,337,130 IRR among drivers of public vehicles. Statistical value of life was estimated 118,222,552,601,648 IRR, for according to 4,694 dead drivers, which was equivalent to 3,940,751,753 $ based on the dollar free market rate of 30,000 IRR (purchase power parity). Injury cost was 108,376,366,437,500 IRR, equivalent to 3,612,545,548 $. In sum, injury and death cases came to 226,606,472,346,449 IRR, equivalent to 7,553,549,078 $. Moreover in 2013, cost of traffic injuries among the drivers of public vehicles constituted 1.25% of gross national income, which was 604,300,000,000$. WTP had a significant relationship with gender, daily payment, more payment for time reduction, more pay to less traffic, and minibus drivers. CONCLUSION: Cost of traffic injuries among drivers of public vehicles included 1.25% of gross national income, which was noticeable; minibus drivers had less perception of risk reduction than others.

Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.

In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.

CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test.

Inferring Gene Regulatory Networks (GRNs) from gene expression data is a major challenge in systems biology. The Path Consistency (PC) algorithm is one of the popular methods in this field. However, as an order dependent algorithm, PC algorithm is not robust because it achieves different network topologies if gene orders are permuted. In addition, the performance of this algorithm depends on the threshold value used for independence tests. Consequently, selecting suitable sequential ordering of nodes and an appropriate threshold value for the inputs of PC algorithm are challenges to infer a good GRN. In this work, we propose a heuristic algorithm, namely SORDER, to find a suitable sequential ordering of nodes. Based on the SORDER algorithm and a suitable interval threshold for Conditional Mutual Information (CMI) tests, a network inference method, namely the Consensus Network (CN), has been developed. In the proposed method, for each edge of the complete graph, a weighted value is defined. This value is considered as the reliability value of dependency between two nodes. The final inferred network, obtained using the CN algorithm, contains edges with a reliability value of dependency of more than a defined threshold. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The results indicate that the CN algorithm is suitable for learning GRNs and it considerably improves the precision of network inference. The source of data sets and codes are available at .

Estimating cost of road traffic injuries in Iran using willingness to pay (WTP) method.

We aimed to use the willingness to pay (WTP) method to calculate the cost of traffic injuries in Iran in 2013. We conducted a cross-sectional questionnaire-based study of 846 randomly selected road users. WTP data was collected for four scenarios for vehicle occupants, pedestrians, vehicle drivers, and motorcyclists. Final analysis was carried out using Weibull and maximum likelihood method. Mean WTP was 2,612,050 Iranian rials (IRR). Statistical value of life was estimated according to 20,408 fatalities 402,314,106,073,648 IRR (US$13,410,470,202 based on purchasing power parity at (February 27th, 2014). Injury cost was US$25,637,870,872 (based on 318,802 injured people in 2013, multiple daily traffic volume of 311, and multiple daily payment of 31,030 IRR for 250 working days). The total estimated cost of injury and death cases was 39,048,341,074$. Gross national income of Iran was, US$604,300,000,000 in 2013 and the costs of traffic injuries constituted 6·46% of gross national income. WTP was significantly associated with age, gender, monthly income, daily payment, more payment for time reduction, trip mileage, drivers and occupants from road users. The costs of traffic injuries in Iran in 2013 accounted for 6.64% of gross national income, much higher than the global average. Policymaking and resource allocation to reduce traffic-related death and injury rates have the potential to deliver a huge economic benefit.

IPCA-CMI: an algorithm for inferring gene regulatory networks based on a combination of PCA-CMI and MIT score.

Inferring gene regulatory networks (GRNs) is a major issue in systems biology, which explicitly characterizes regulatory processes in the cell. The Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI) is a well-known method in this field. In this study, we introduce a new algorithm (IPCA-CMI) and apply it to a number of gene expression data sets in order to evaluate the accuracy of the algorithm to infer GRNs. The IPCA-CMI can be categorized as a hybrid method, using the PCA-CMI and Hill-Climbing algorithm (based on MIT score). The conditional dependence between variables is determined by the conditional mutual information test which can take into account both linear and nonlinear genes relations. IPCA-CMI uses a score and search method and defines a selected set of variables which is adjacent to one of X or Y. This set is used to determine the dependency between X and Y. This method is compared with the method of evaluating dependency by PCA-CMI in which the set of variables adjacent to both X and Y, is selected. The merits of the IPCA-CMI are evaluated by applying this algorithm to the DREAM3 Challenge data sets with n variables and n samples (n = 10, 50, 100) and to experimental data from Escherichia coil containing 9 variables and 9 samples. Results indicate that applying the IPCA-CMI improves the precision of learning the structure of the GRNs in comparison with that of the PCA-CMI.

Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: a Bayesian approach.

Joint modeling of longitudinal data and survival data has been used widely for analyzing AIDS clinical trials, where a biological marker such as CD4 count measurement can be an important predictor of survival. In most of these studies, a normal distribution is used for modeling longitudinal responses, which leads to vulnerable inference in the presence of outliers in longitudinal measurements. Powerful distributions for robust analysis are normal/independent distributions, which include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions in addition to the normal. In this paper, a linear-mixed effects model with normal/independent distribution for both random effects and residuals and Cox's model for survival time are used. For estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. Some simulation studies are performed for illustration of the proposed method. Also, the method is illustrated on a real AIDS data set and the best model is selected using some criteria.

Hypothermia in Iranian newborns. Incidence, risk factors and related complications.

OBJECTIVE: To determine the incidence rate and factors associated with hypothermia in Iranian newborns and to discover the effect of hypothermia on neonatal morbidity and mortality. METHODS: We selected a random sample of 1952 neonates using a multistage sampling technique from February 2004 to February 2005 in University Teaching Hospitals in Iran. We measured repeatedly at different time points the rectal temperature of these newborns. At each time of measurement, those with rectal temperature <36 degrees C were considered as hypothermic. RESULTS: The obtained results showed that approximately one third of newborns became hypothermic immediately after birth. In addition, the regression analysis revealed that low birth weights, prematures, low apgar scores, infants of multiple pregnancies and those who received cardiopulmonary resuscitation had higher risk for being hypothermic. It was also found that hypothermia increases the risk of metabolic acidosis, jaundice, respiratory distress, hypoglycemia, pulmonary hemorrhage and death, regardless of the newborn's weight and gestational age. CONCLUSION: There is an urgent need to train mothers and all levels of neonatal care staff to control this health problem in our country.