Showing 2 results for Tlgs
M Sedehi, Y Mehrabi, A Kazemnejad, V Joharimajd, F Hadaegh,
Volume 6, Issue 4 (3-2011)
Abstract
Background & Objective: Mixed outcomes arise when, in a multivariate model, response variables measured on different scales such as binary and continuous. Artificial neural networks (ANN) can be used for modeling in situations where classic models have restricted application when some of their assumptions are not met. In this paper, we propose a method based on ANNs for modeling mixed binary and continuous outcomes.
Methods: Univariate and bivariate models were evaluated based on two different sets of simulated data. The scaled conjugate gradient (SCG) algorithm was used for optimization. To end the algorithm and finding optimum number of iteration and learning coefficient, mean squared error (MSE) was computed. Predictive accuracy rate criterion was employed for selection of appropriate model. We also used our model in medical data for joint prediction of metabolic syndrome (binary) and HOMA-IR (continues) in Tehran Lipid and Glucose Study (TLGS). The codes were written in R 2.9.0 and MATLAB 7.6.
Results: The predictive accuracy for univariate and bivariate models based on simulated dataset Ι, where two outcomes associated with a common covariate, were shown to be approximately similar. However, in simulated dataset ΙΙ in which two outcomes associated with different covariates, predictive accuracy in bivariate models were seen to be larger than that of univariate models.
Conclusions: It is indicated that the predictive accuracy gain is higher in bivariate model, when the outcomes share a different set of covariates with higher level of correlation between the outcomes.
N Shakeri, F Eskandari, F Hajsheikholeslami, Aa Momenan, F Azizi,
Volume 9, Issue 3 (2-2014)
Abstract
Background & Objectives: Although the population of elderly is increasing in Iran, few studies carried out on this group. The aim of this study was to identify life expectancy and contributory risk factors for the Tehranian elderly of ages above 60 years.
Methods: Individuals above 60 years old whom were recruited in the primary phase of the Tehran Lipid and Glucose Study (TLGS) during 1998-2001 were followed up for 12 years and their vital status were registered (1998-2011). Age and sex mortality rates for age groups (60-69, 70-79, 80+) were calculated and by using Cox proportional hazard model the mean of survival time and hazard rates with respect to risk factors were estimated.
Results: Life expectancy for females and males after crossing 60 years of age reaches to 81 and 80 years, respectively without any statistically significant differences between these two groups. Cox model showed that diabetes, BMI>33Kg/m2 and non ischmecic heart disease reduced survival time in women significantly. While diabetes, smoking, hypertension, ischemic heart disease, history of MI, stroke or sudden death of father, brother or son, lack of physical activity and antihypertensive medications are among the hazardous risk factors for men.
Conclusion: Among the variables studied, only three (ABC) of them were found as risk factors of women's life, while for men seven risk factors were identified. It seems that more studies are needed to determine the risk factors for women.
