Publications (3)4.84 Total impact
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Article: Quality of life of childbearing age women and its associated factors: an application of seemingly unrelated regression (SUR) models.
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ABSTRACT: PURPOSE: This article is a report of using seemingly unrelated regression (SUR) models to examine the determinants of different dimensions of quality of life (QoL) among childbearing age women. There are a limited number of studies on QoL and its associated factors among women in developing countries such as Iran. Therefore, more attention should be focused on identifying these issues. METHODS: We administered the Persian's abbreviated version of the World Health Organization Quality of Life (WHOQOL-BREF) questionnaire to 1,067 married women aged between 15 and 49 years. The women were chosen via a multistage research design from the rural region of Shiraz, the center of Fars Province in Iran in 2008. Clinical and socio-demographic characteristics as well as their reproductive health-related characteristics were investigated. To identify associated factors of QoL dimensions, ordinary least squares (OLS) regression and SUR were used and their findings were compared. RESULTS: The WHOQOL-BREF showed acceptable consistency (Cronbach's alpha range: 0.62-0.75 across domains). Lower age, absence of long-term illness, economic status satisfaction, higher level of education, lower number of pregnancies, and higher body mass index were important associated factors of different dimensions of the QoL among these women. The estimated parameters for these factors were in close agreement in both OLS and SUR estimation methods. However, the SUR estimator provided the higher precision of the estimates than the OLS estimator, as the parameters obtained by SUR are characterized by lower standard errors. Women's age, income satisfaction, and level of education were common for all domains. CONCLUSIONS: This study presents a novel approach to simultaneously predict QoL domains using the SUR estimators and the results are relevant for implementing objective QoL. SUR estimators performed consistently better than the OLS estimators, since SUR takes the correlation between error terms into account. Thus, the SUR method could be a useful methodology for predicting QoL domains.Quality of Life Research 08/2012; · 2.30 Impact Factor -
Article: Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.
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ABSTRACT: Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.Computational and Mathematical Methods in Medicine 01/2012; 2012:821643. · 0.68 Impact Factor -
Article: A parametric method for cumulative incidence modeling with a new four-parameter log-logistic distribution.
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ABSTRACT: Competing risks, which are particularly encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data. One feature of competing risks is the cumulative incidence function, which is modeled in most studies using non- or semi-parametric methods. However, parametric models are required in some cases to ensure maximum efficiency, and to fit various shapes of hazard function. We have used the stable distributions family of Hougaard to propose a new four-parameter distribution by extending a two-parameter log-logistic distribution, and carried out a simulation study to compare the cumulative incidence estimated with this distribution with the estimates obtained using a non-parametric method. To test our approach in a practical application, the model was applied to a set of real data on fertility history. The results of simulation studies showed that the estimated cumulative incidence function was more accurate than non-parametric estimates in some settings. Analyses of real data indicated that the proposed distribution showed a much better fit to the data than the other distributions tested. Therefore, the new distribution is recommended for practical applications to parameterize the cumulative incidence function in competing risk settings.Theoretical Biology and Medical Modelling 11/2011; 8:43. · 1.86 Impact Factor
