Abstract: When the nature of a data set comes from a skew distribution, the use of usual Gaussian mixed effect model can be unreliable. In recent years, skew-normal mixed effect models have been used frequently for longitudinal data modeling in many biomedical studies. These models are flexible for considering skewness of the longitudinal data. In this paper, a shared parameter model is considered for simultaneously analysing nonignorable missingness and skew longitudinal outcomes. 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 methods. The proposed methods are applied for analyzing an AIDS data set, where CD4 count measurements are gathered as longitudinal outcomes. In these data CD4 counts measurements are severely skew. In application section, different structures of skew-normal distribution assumptions for random effects and errors are considered where deviance informationcriterion is used for model comparison.

Keywords: Bayesian approach, Longitudinal data, Markov Chain Monte Carlo, Missingness mechanism, Nonignorable missing data, Random effects model.

Abstract: Aim: The goal of this study is to specify the risks, family and environmental factors affecting smoking behavior and develop suggestions for Turkish individuals by considering sibling data.

Materials and Methods:The data was collected by voluntary senior year students attending Kırıkkale University, Department of Statistics. The sample of 751 families was selected from families with at least two children. Each sibling’s socio-demographic information and behavioral phenotypes were collected using a survey from both siblings. We selected one of siblings randomly as ‘sibling1’ and defined the other sibling as ‘sibling2’. Hypothesis testing and multivariable clustered logistic regression models were used to evaluate the data and find the optimum model by using dependent sibling data.

Results: Out of 1502 (751 pairs) siblings 843 (56.1%) were males, 659 (43.9%) were females. According to the survey results, 508 of the males (67.7%) and 242 of the females (32.3%) were smokers for a month or longer and smoked every day. The risk of smoking was 2.26 times higher in males than in females. Having a smoking sibling increased the risk of smoking 1.95 times, alcohol using increased the risk 2.11 times. We found that when the age difference between siblings is 0-7 years, having a same sex sibling who smokes increases one’s risk 4.7 times in females and 5 times in males; when the siblings are of different sexes, according to these age differences

Conclusion: The survey showed that the gender and sibling’s and parent’s smoking both play a significant role on smoking behavior. But children seem to learn smoking from their siblings more than from parents. Having same sex sibling who smokes plays significant role in smoking behavior for both males and females.

Keywords: Clustered logistic regression, FTND (Fagerström Test for Nicotine Dependence), Nicotine dependence, Paired data, Smoking status, Siblings, Turkey.