Iranian Journal of

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Background and objective: Cigarette smoking in adolescent population seems a major public health issue. The goal of the present study was to identify the effect of socio-personal factors on transitions in the adolescent smoking.
Methods: A total of 1785 students were assessed twice during 12 months. with respect to stage of change. The predictor variables were measured when the students were in the 10th grade. Logistic regression was used to predict different smoking stages at grade 11.
Results: At the end of follow-up, 14.3 percent of non-smokers have had smoking experiment, and 16.5 percent of smokers have showed to be the regular smokers. Among non smokers, using alcohol, friendship with smoker group were predictors of being regular smokers.
Conclusion: Smoking prevention program should begin in adolescent age group.

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Background & Objectives: Peritoneal dialysis is one of the most common types of dialysis in patients with renal failure. However multivariate analysis such as log- rank test and Cox have usually used to evaluate association of risk factors in survival of this group of patients, the aim of this study was to perform of Weibull, Gamma, Lognormal and Logistic Mixture cure models in survival analysis of these patients.
Methods: Data of 433 patients undergoing CAPD who registered in two centers in Tehran, Iran between 1997 to 2009 were used in this analysis. We investigated center, gender, age, cholesterol, Low Density Lipoprotein (LDL), High density lipoprotein (HDL), triglyceride, albumin, hemoglobin, creatinine, Fasting Blood Sugar (FBS), calcium and phosphorous as variables effect with Kaplan-Meier and cure model. CUREREGR module was used for survival analysis.
Results: Comparison of AIC (Akaike Information Criterion) of Weibull, Gama, Lognormal and Logistic Mixture cure models showed that Weibull distribution AIC is lower for almost all variables than other distributions. Weibull distribution has better fitness for data than others. In the multivariate Weibull model, age and albumin variables had significant effect on long-term survival of patients (P<0.01). Triglycerides effect on long-term survival had borderline (P = 0.065). Also HDL, FBS and calcium were significant on short term survival (P<0.01) but significance of LDL was borderline (P=0.088).
Conclusion: Cure models have the ability to analyze dialysis patients' survival data and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate and would help to make better prediction for patients by health care professionals.

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Background & Objectives: One of the problems of diagnostic accuracy studies is verification bias. It occurs when standard test performed only for non-representative subsample of study subjects that diagnostic test done for them. In this study we extend a Bayesian method to correct this bias.
Methods: Patients that have had at least twice repeated failures in cycles IVF ICSI were included in this model. Patients were screened by using an ultrasonography and those with polyps recommended for hysteroscopy. A logistic regression with binomial outcome fit to predict the missing values (false and true negative), sensitivity and specificity. Bayesian methods was applied with informative prior on polyp prevalence. False and true negatives were estimated in Bayesian framework.
Results: A total of 238 patients were screened and 47 had polyps. Those with polyps are strongly recommended to undergo hysteroscopy, 47/47 decided to have a hysteroscopy and 37/47 were confirmed to have polyps. None of the 191 patients with no polyps in ultrasonography had hysteroscopy. The false negative was obtained 14 and true negative 177, so sensitivity and specificity was estimated easily after estimating missing data. Sensitivity and specificity were equal to 74% and 94% respectively.
Conclusion: Bayesian analyses with informative prior seem to be powerful tools in simulation experimental