Journal of Biostatistics and Epidemiology
Volume:2 Issue: 1, Winter 2016

The aim of this study is to introduce a parametric mixture model to analysis the competing-risks data with two types of failure. In mixture context, ith type of failure is ith component. The baseline failure time for the first and second types of failure are modeled as proportional hazard models according to Weibull and Gompertz distributions, respectively. The covariates affect on both the probability of occurrence and the hazards of the failure types. The probability of occurrence is modeled to depend on covariates through the logistic model. The parameters can be estimated by application of the expectation-conditional maximization and Newton-Raphson algorithms. The simulation studies are performed to compare the proposed model with parametric cause-specific and Fine and Gray models. The results show that the proposed parametric mixture method compared with other models provides consistently less biased estimates for low, mildly, moderately, and heavily censored samples. The analysis of post-kidney transplant malignancy data showed that the conclusions obtained from the mixture and other approaches have some different interpretations.

Stroke in young adults is rare but can be devastating for the affected individuals and their families. Some triggers of stroke may be acute but transient effects on the pathophysiological condition while other factors may be effective overa longer period.
Methods & Materials:This study was a case-crossover study on 18 young adults. The study included patients aged 15-49 years who hospitalized for ischemic stroke for the first time from June 2012 to September 2013. In this study, mental health status was considered during the 6-month period so that exposure within 1 month of stroke onset (hazard period) was compared with exposure during five control periods of 1 month preceding the hazard period.
Conditional logistic regression showed there was an association between mental health and stroke so that for every 5 unit increase in mental health, odds of stroke will increase about 13-fold.In the other words, much higher scores on mental health, mental health condition weaker.
Mental health status is associated with the occurrence of ischemic stroke in young adults so that whatever mental health condition weaker odds of ischemic stroke incidence is high.

Congenital hypothyroidism (CH) is one of the most common endocrine diseases and is a major cause of preventable mental retardation. Early diagnosis of CH can help prevent future diseases. Although time series techniques are often utilized to forecast future status, they are inadequate to deal with count data with overdispersion. The aim of this study was to apply Poisson hidden Markov model to forecast new monthly cases of CH disease.
Methods & Materials:This study was based on the monthly frequency of new CH cases in Khuzestan province of Iran, from 2008 to 2014. We applied stationary Poisson hidden Markov with ifferent states to determine the number of states for the model. According to the model, with the specified state, new CH cases were forecast for the next 24 months.
The Poisson hidden Markov with two states based on Akaike information criterion was chosen for the data. The results of forecasting showed that the new CH cases for the next 2 years comforted in state two with the frequency of new cases at 6-18. The forecast mode and median for all months were 12 and 13, respectively.
Our estimates indicated that state of frequency of CH case is invariant during the forecast time. Forecast means for the next 2 years were from 13 to 14 new CH cases. Furthermore, forecasting intervals were observed between 7 and 25 new cases. These estimates are valid when the general fertility rate and crude birth rate were been fixed.

A meta-analysis refers to the statistical synthesis of results from a series of studies and has been used to estimate pooled prevalence of human diseases. In this study, we review some statistical issues regarding the meta-analysis of the prevalence of human diseases such as statistical software and programs, transformations of prevalence rate, assessment of heterogeneity, and publication bias.

Chronic diseases impact not only on patients but also on their family members’ lives. This study aims to determine dimensions of Family Dermatology Life Quality Index (FDLQI) questionnaire by the use of classic and Bayesian factor analysis (BFA) factor.
Methods & Materials:In this study, FDLQI questionnaire distributed among 100 family members of dermatological patients. BFA is exploited to determining dimension and contribution of items of the questionnaire in different sample size. BFA is examined using the Monte Carlo-Markov chain algorithm. All the above analysis is done in sample size 100, 130, 150.
In this study, 100 family members of dermatological patient attended to evaluate Persian version of FDLQI questionnaire. A mean age of participants was 37.1 years (± 12.3). A mean score of FDLQI was 15.4 (± 5.5) with maximum and minimum scores of 30 and 6, respectively.Exploratory FA revealed a one-factor solution that accounted for 45.87% of the total variance. The unidimensional model was concordance by confirmatory FA. For more exploration, BFA was performed. Two factors extracted when iteration is done.
It appears that when sample size diminished, Cronbach’s alpha and Kaiser–Meyer–Olkin increased. Among 10 items of the questionnaire, item 9 mostly appears differently in results.

The main objective of this study is to find the prevalence, level of knowledge, and mode of transmission of reproductive tract infection (RTI)/sexually transmitted infection (STI) among evermarried females in Uttar Pradesh. To study the various socio-economic and demographic factors responsible for RTI/STI among married females. This study is based on data extracted from District Level Health Survey III in Uttar Pradesh for ever-married females age 15-49 years. RTI/STI prevalence in UP is 29%. Most common symptom of RTI/STI is unusual vaginal discharge. Most of the RTI/STI infected females sought treatment in the private medical sector. The main source of information about RTI/STI is relatives/friends. 60% women do not know any mode of transmission of RTI/STI among those who have heard of RTI/STI.

Each semester, students are asked to evaluate the academic staff through an online questionnaire. Generalized estimating equations model (GEE), taking into account the correlation between scores, is the established tool to analyze longitudinal data. The aim of this manuscript is to identify characteristics that influence staff score and to address the importance of selection of appropriate correlation structure. We analyzed scores of 336 staff in six consecutive semesters applying GEE with three correlation structures: exchangeable, autoregressive, and unstructured. We also compared the performance of these correlation structures via simulation study. Three normally distributed outcomes with exchangeable correlation structure were simulated. Four independent variables (two continuous and two binary) of which only one was related to the outcome were generated. In the empirical data set, time and academic degree were positively correlated with staffs’ score. Our simulation study showed that the probability that autoregressive and unstructured correlation structures select wrong predictors as being significant is 1.3% and 3.7%. We concluded tha evaluation of staff by students improved the quality of education. In addition, selection of inappropriate correlation structure affects the significance of variables.

In this study, an effort has been made to determine the most important risk factors of tuberculosis (TB) in district Mardan. A total of 645 cases were examined, and their personal and medical data were collected. For each case, the phenomenon of TB was studied in relation to different risk factors. Statistical techniques of logistic regression and backward elimination procedure were used to analyze the data and to determine a parsimonious model. For both male and female cases, the final selected logistic regression model contain the risk factors: sex, residence, household population, diet, medical care, and close contact with infectious patients as well as a joint effect of two factors and three factors, namely, medical care and marital status; and economic status, and medical care and marital status. Separate logistic regression was then fitted for each sex using the same procedure. For male cases, the final selected logistic regression model contains risk factors: residence, diet, and close contact with infectious patients as well as a combined effect of two factors, namely, economic status and diet, medical care and diet. For female cases, the final selected logistic regression model contains the risk factors: household population, economic status, diet, and close contact with infectious patients as well as a combined effect of two factors, namely, medical care and close contact with an infectious patient.