A two-stage approach for joint modeling of longitudinal measurements and competing risks data

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

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.

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.

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.

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.

Molecular detection of Trypanosoma evansi based on ITS1 rDNA gene in Camelus dromedarius in Sistan Region, Iran

Trypanosomiasis is a disease caused by a flagellate protozoon called Trypanosoma and can be mechanically transmitted by vectors to humans and animals. Various species of Trypanosoma are found in livestock and poultry, which include Trypanosoma evansi, T. brucei, T. vivax and T. congolense. The camel is the most sensitive livestock for T. evansi, so the exact identification of infection is very important for epidemiological studies and the design of control programs. The present study was conducted with the aim of molecular detection of camel trypanosomiasis in the Sistan region in 2015. Previous studies have shown that internal transcribed spacer one (ITS1) of the ribosomal DNA is a reliable genetic marker for carrying out systematic molecular studies of trypanosomes. In order to investigate infections of camels with T. evansi, a total of 113 blood samples were collected randomly and the presence of parasites in each sample was evaluated using the microscopic method and polymerase chain reaction (PCR) test. Genomic DNA was extracted and the ITS-1 was amplified by PCR. In comparison to the nucleotide sequence obtained with the sequences recorded in GenBank, it was determined that there is a 99% homology with the recorded sequence of T. evansi. The obtained sequence was registered in Gen Bank with kx900449 code. The T. evansi sequences from different countries such as India, Taiwan, Thailand, the Philippines, China and Argentina and etc., were extracted from the Gene bank and aligned using the ClustalW2 sequence alignment tool and MEGA software. In this study the prevalence of T. evansi infection using the molecular method was 6.19% and no positive samples were found by microscopic observation.

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 (1^st September–1^st 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.

The common structure of several models for non-ignorable dropout

This paper presents a multivariate generalization of the classical Heckman selection model and applies it to non-ignorable dropout in repeated continuous responses. Many of the recent models for dropout in repeated continuous responses can be written as special forms of this generalized Heckman model. To illustrate this, we present the parameterizations needed to obtain the form of dropout model that occurs when (1) the separate models for the response and dropout are linked by common random parameters, (2) the dropout model is an explicit function of the previous responses and the possibly unobserved current response, (3) the dropout model is both a function of the current response and a common random parameter, and (4) there is a covariance between the stochastic disturbances of the response and dropout processes. We present the joint likelihood of the generalized Heckman model and a residual for the responses. We contrast two of the dropout models in a simulation study. We compare the results obtained from several dropout models on the well known mastitis data.

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.