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.
In this paper, we utilize a general linear model for analyzing data with missing values in some covariates and response variable. Our aim is to fit a general linear model and to construct a ...confidence region for the parameters of the general linear model based on the empirical likelihood ratio function. Also, we assume that missing data may happen in covariates or in response variable or in both of them with missing not at random mechanism where the probability of missing a datum is specified by a logistic model. We use inverse probability weights and an augmented method as the auxiliary condition of empirical likelihood to estimate parameters of the general linear model. Asymptotic properties of the empirical log-likelihood ratio are investigated whether the exponential tilting parameter is known or estimated by the follow-up sample. The asymptotic normality of estimators is also proved. Some simulation studies are used to illustrate the performance of our model for different sample sizes. Also, a real dataset is studied by the proposed methods.
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 .
Longitudinal studies often involve multiple mixed response variables measured repeatedly over time. Although separate modeling of these multiple mixed response variables can be easily performed, they ...may lead to inefficient estimates and consequently, misleading inferences. For obtaining correct inference, one needs to model multiple mixed responses jointly. In this paper, we use copula models for defining a multivariate distribution for multiple mixed outcomes at each time point. Then, we use transition model for considering association between longitudinal measurements. Two simulation studies are performed for illustration of the proposed approach. The results of the simulation studies show that the use of the separate models instead of the joint modeling leads to inefficient parameter estimates. The proposed approach is also used for analyzing two real data sets. The first data set is a part of the British Household Panel Survey. In this data set, annual income and life satisfaction are considered as the continuous and the ordinal correlated longitudinal responses, respectively. The second data set is a longitudinal data about heart failure patients. This study is a treatment–control study, where the effect of a treatment is simultaneously investigated on readmission and referral to doctor as two binary associated longitudinal responses.
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.
In this paper, we present a model based on pair copula construction for bivariate longitudinal mixed ordinal and continuous responses. The temporal association of each response is separately modeled ...using pair copula construction with a D-vine structure and the contemporaneous association of bivariate responses is then joined by a bivariate copula. We employ a sequential approach for inference and its performance is investigated by a simulation study. Moreover, the proposed model is applied to Peabody Individual Achievement Test (PIAT) dataset which examines the relationship between reading capability and antisocial behavior of children. The result is that, children with low levels of antisocial behavior have better reading ability than that of children with high levels of antisocial behavior.
The microdata of surveys are valuable resources for analyzing and modeling relationships between variables of interest. These microdata are often incomplete because of nonresponses in surveys and, if ...not considered, may lead to model misspecification and biased results. Nonresponse variable is usually assumed as a binary variable, and it is used to construct a sample selection model in many researches. However, this variable is a multilevel variable related to its reasons of occurring. Missing mechanism may differ among the levels of nonresponse, and merging the levels of nonresponse may cause bias in the results of the analysis. In this paper, a method is proposed for analyzing survey data with respect to reasons for the nonresponse based on sample selection model. Each nonresponse level is considered as a selection rule, and classical Heckman model is extended. Simulation studies and an analysis of a real data set from an establishment survey are presented to demonstrate the performance and practical usefulness of the proposed method.
* In most of the longitudinal studies, involving count responses, excess zeros are common in practice. Usually, the current response measurement in a longitudinal sequence is a function of previous ...outcomes. For example, in a study about acute renal allograft rejection, the number of acute rejection episodes for a patient in current time is a function of this outcome at previous follow-up times. In this paper, we consider a transition model for accounting the dependence of current outcome on the previous outcomes in the presence of excess zeros. We propose the use of the generalized Poisson distribution as a flexible distribution for considering overdispersion (or underdispersion). The maximum likelihood estimates of the parameters are obtained using the EM algorithm. Some simulation studies are performed for illustration of the proposed methods. Also, analysis of a real data set of a kidney allograft rejection study illustrates the application of the proposed model. Key-Words: * count data; EM algorithm; generalized Poisson distribution; longitudinal data; transition models; zero-inflated models. AMS Subject Classification: * 62J99, 62P10.
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.
Joint modeling of longitudinal measurements and survival time has an important role in analyzing medical data sets. For example, in HIV data sets, a biological marker such as CD4 count measurements ...is considered as a predictor of survival. Usually, longitudinal responses of these studies are severely skew. An ordinary method for reducing the skewness is the use of square root or logarithm transformations of responses. In most of the HIV data sets, because of high rate of missingness, skewness is remained even after using the transformations. Therefore, a general form of distributions for considering skewness in the model should be used. In this paper, we have used multivariate skew-normal distribution to allow a flexible model for considering non-symmetrically of the responses. We have used a skew-normal mixed effect model for longitudinal measurements and a Cox proportional hazard model for time to event variable. These two models share some random effects. A Bayesian approach using Markov chain Monte Carlo is adopted for parameter estimation. Some simulation studies are performed to investigate the performance of the proposed method. Also, the method is illustrated using a real HIV data set. In these data, longitudinal outcomes are skew and death is considered as the event of interest. Different model structures are developed for analyzing this data set, where model selection is performed using some Bayesian criteria. Key-Words: * Bayesian approach; Cox proportional model; joint modeling; longitudinal data; skew-normal distribution; time to event data.
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