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Showing 2 results for Karimlou
Comparison of bayesian with the classical methods in estimating parameters of logistic regression and its application in respiratory
M Karimlou , K. Mohammad , M. R Meskhani , G.r Jandaghi , K Nouri , E Pasha , K Azam ,
Volume 4, Issue 2 (3 2006)
Abstract
Background and Aim: Logistic regression is an analytic tool widely used in medical and epidemiologic research. In many studies, we face data sets in which some of the data are not recorded. A simple way to deal with such "missing data" is to simply ignore the subjects with missing observations, and perform the analysis on cases for which complete data are available.
Materials and Methods: We consider methods for analyzing logistic regression models with complete data recorded for some covariates (Z) but missing data for other covariates (X). When data on X are Missing At Random (MAR), we present a likelihood approach for the observed data that allows the analysis as if the data were complete.
Results: By this approach, estimation of parameters is done using both Maximum Likelihood and Bayesian methods through the Markov Chain Monte Carlo numerical computation scheme and the results are compared. The illustrative example considered in this article involves data from lung auscultations as part of a Health Survey in Tehran.
Conclusion: In comparing different methods, Bayesian estimates using the model described in this study are much closer to those generated by analysis of the full data by the standard model.
Trend of age at first marriage of women in Tehran province, Iran and its comparison with the national trend, based on the Health and Disease Survey
M Karimlou , K Mohammad , K Azam , M.a Noorbala ,
Volume 5, Issue 1 (2 2007)
Abstract

Background and Aim: The present article attempts to define the current trend for age at first marriage, based on the nationwide Health Survey of 1999 and using the Brass model.

Material and Methods: The national Health survey was conducted in 1999 and involved 1/1000 of the total population, selected via cluster sampling (clusters of 8 households each).

The sample used for the present study consisted of 16000 women aged 15-49, including 3026 women in Tehran province. Variables used for analysis were current age, marital status, age at first marriage and residential area (urban/rural). The mean age at first marriage for married women was 17.8 years (sd=3.7) for the whole country, and 18.2 years (sd=3.7) for Tehran province.

Results: The Brass model fitted to the data revealed a significant decreasing trend for the proportion of married women in all age groups, especially in the 15-19 year-old category.

Conclusion: This obviously indicates an upward trend for age at first marriage.


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