Journal of Biostatistics and Epidemiology
Volume:1 Issue: 3, Summmer 2015

Multi-state models can help better understand the process of chronic diseases such as cancers. These models are influenced by assumptions like individual homogeneity. This study aimed to investigate the effect of lack of individual homogeneity assumption in multi-state models.Methods & Materials: To investigate the effect of lack of individual homogeneity assumption in multi-state models, tracking model as well as frailty factor with gamma distribution were used. Accordingly, without any simulation and only based on asymptotic theory, the bias of mean transition rate which is among the basic parameters of the multi-state models was studied. Analysis of the effect of individual homogeneity assumption misspecification revealed that for different number of follow-ups as well as censoring time, the mean transition rate and its variance were underestimated. In addition, if there is a lot of heterogeneity in reality and if the individual homogeneous multi-state model is fitted, a significant bias will exist in the estimated mean transition rate and its variance. The results of this study also showed that the intensity of bias increases with an increase in the degree of heterogeneity. But with an increase in the number of follow-ups, the intensity of bias decreases, to some extent. Disregarding individual homogeneity assumption in a heterogeneous population causes bias in the estimation of multi-state model parameters and with an increase in the degree of heterogeneity, the intensity of bias will increase too.

This study aimed to estimate and project the current and future disability burden of typhoid fever in Iran associated with climate and population to provide best policies for climate change adaptation.Methods & Materials: Years lost due to disabilities (YLDs) were measured as burden estimation in this study. The temperature was selected as climate variable. Future temperature rising (projected for 2030 and 2050) used according to Intergovernmental Panel on Climate Change reports. Typhoid fever incidence in 2010 applied as the baseline data for YLDs calculation. The previous published regression models were considered for YLDs’ future projections. Furthermore, the future demographic change was included for YLDs calculation. Compared with the YLDs in 2010, increasing temperature and demographic change may lead to a 5.5-9% increase in the YLDs by 2030 and a 13.7-22% increase by 2050 if other factors remain constant. The highest YLDs was projected for > 45 years old (56.3%) in 2050 under temperature rising and population change scenario. Climate change and aging may impact on burden of typhoid fever in the future. Adaptive strategies should be considered to prevent and reduce the health burden of climate change.

One of the common used models in time series is auto regressive integrated moving average (ARIMA) model. ARIMA will do modeling only linearly. Artificial neural networks (ANN) are modern methods that be used for time series forecasting. These models can identify non-linear relationships among data. The breast cancer has the most mortality of cancers among women. The aim of this study was fitting the both ARIMA and ANNs models on the breast cancer mortality and comparing the accuracy of those in parameter estimating and forecasting.Methods & Materials: We used the mortality of breast cancer data for comparing two models. The data are the number of deaths caused by breast cancer in 105 months in Kerman province. Each of ARIMA and ANNs models is fitted and chose the best one of each method separately, with some diagnostic criteria. Then, the performance of them is compared a minimum of mean squared error and mean absolute error. This comparison shows that the performance of ANNs models in parameter estimating and forecasting is better than ARIMA model. It seems that the breast cancer mortality has a non-linear pattern, and the ANNs approach can be more useful and more accurate than ARIMA method.

Assessment could be assumed as a valuable mean of highlighting the organization strengths and spotting its weaknesses. Academies are not exceptions in this regard. Knowing the items, which entail more concentrated attention, the leadership of the academy will shift the resources to compensate the extenuations. This study aimed to provide the Iran Academy of Medical Sciences (AMS) a model of assessment and development of its credibility.Methods & Materials: Reviewing the scientific literatures about the components of credibility of an organization, three components were elected, 1. Structure, 2. Performance, and 3. Acceptability. Assessing this academy, a framework for summarizing the information of other academies was developed. For the next steps, to improve the quality of the framework and to study more AMS, we decided to search the internet for more countries and academies. We find that 16 indices and their 77 measures could be used to assess the AMS. Establishing a well-defined system with a trained staff devoted to assess the AMS activities, would be in the favor of evaluating the AMS annually; and by publication of strategic reports, AMS strengths would be reinforced and its weaknesses would be reformed.

Longitudinal study plays an important role in the epidemiological, clinical, and social science studies. In these kinds of studies, every individual is observed frequently during a period of time. The statistical analysis of longitudinal presents special opportunities and challenges. The repeated outcomes for one individual tend to be correlated among themselves also one of the problems that we face in longitudinal studies is the missing data. These two issues are taken into account in this article. By using the logit link function, designed for longitudinal data, we introduce a mixed model, and then present the evaluation of variance components by Bayesian methods. The applied method exploits the non-conjugate priors. The conjugate priors, however, are easier to deal with. Finally, an application of the model in a clinical experiment is presented.

Visceral leishmaniasis (VL) or kala-azar is a parasitic disease caused by the species of Leishmania donovani complex. Mediterranean type of disease is endemic in some parts of Iran and more than 95% of seropositivity cases were reported in children up to 12 years of age. A cross-sectional study was conducted to determine the seroprevalence of VL in nomadic tribe’s population of the Kerman Province.Methods & Materials: Totally, 862 blood samples were collected from children up to 12 years old from nomadic tribes of the studied area. Before sampling, a questionnaire was filled out for each case. All the collected blood samples were examined after the plasma separating by direct agglutination test for detection of anti-Leishmania infantum antibodies. The cut-off titer of ≥ 1:3200 with specific clinical features was considered as VL. Altogether, 25 (2.6%) of the collected plasma samples showed anti-Leishmania antibodies at titers ≥ 1:800 and 6 of them (0.6%) showed titers ≥ 1:3200 with mild clinical manifestations. None of the seropositive cases had a history of kala-azar. Children of 5-8 years old showed the highest seroprevalence rate (4.1%). Also, there were not any significant differences between the rate of seropositivity in males (0.58%) and females (0.67%), (P = 0.225). Although the seroprevalence of VL is relatively low in children up to 12 years old from nomadic tribes of the studied area, due to the importance of the disease, the surveillance system should be monitored by health authorities.

The main assumptions in liner mixed model are normality and independency of random effect component. Unfortunately, these two assumptions might be unrealistic in some situations. Therefore, in this paper, we will discuss about the analysis of Bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, this methodology is illustrated through an application to a triglyceride data from Isfahan’s Mobarakeh Steel Company Cohort Study.

Conditional methods of adjustment are often used to quantify the effect of the exposure on the outcome. As a result, the stratums-specific risk ratio estimates are reported in the presence of interaction between exposure and confounder(s) in the literature, even if the target of the intervention on the exposure is the total population and the interaction itself is not of interest. The reason is that researchers and practitioners are less familiar with marginal methods of adjustment such as inverse-probability-weighting (IPW) and standardization and marginal causal effects which have causal interpretations for the total population even in the presence of interaction. We illustrate the relation between marginal causal effects estimated by IPW and standardization methods and conditional causal effects estimated by traditional methods in four simple scenarios based on the presence of confounding and/or effect modification. The data analysts should consider the intervention level of the exposure for causal effect estimation, especially in the presence of variables which are both confounders and effect modifiers.