Ma Pourhosseingholi, Y Mehrabi, H Alavi-Majd, P Yavari,
Volume 1, Issue 3 (2-2006)
Abstract
Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical response. Strong correlations among explanatory variables (multicollinearity) reduce the efficiency of model to a considerable degree. In this study we used latent variables to reduce the effects of multicollinearity in the analysis of a case-control study.
Methods: Our data came from a case-control study in which 300 women with breast cancer were compared to 300 controls. Five highly correlated quantitative variables were selected to assess the effect of multicollinearity. First, an ordinary logistic regression model was fitted to the data. Then, to remove the effect of multicollinearity, two latent variables were generated using factor analysis and principal components analysis methods. Parameters of logistic regression were estimated using these latent as explanatory variables. We used the estimated standard errors of the parameters to compare the efficiency of models.
Results: The logistic regression based on five primary variables produced unusual odds ratio estimates for age at first pregnancy (OR=67960, 95%CI: 10184-453503) and for total length of breast feeding (OR=0). On the other hand, the parameters estimated for logistic regression on latent variables generated by both factor analysis and principal components analysis were statistically significant (P<0.003). The standard errors were smaller than with ordinary logistic regression on original variables. The factors and components generated by the two methods explained at least 85% of the total variance.
Conclusions: This research showed that the standard errors of the estimated parameters in logistic regression based on latent variables were considerably smaller than that of model for original variables. Therefore models including latent variables could be more efficient when there is multicollinearity among the risk factors for breast cancer.
H Sajadi, M Vameghi, F Mohammadi Shahboulaghi , D Ali, Sh Mohaqeqi Kamal ,
Volume 14, Issue 1 (6-2018)
Abstract
Background and Objectives: Children’s well-being is a multidimensional construct that precedes various aspects of children's lives. This study sought to identify the main areas of children's wellbeing in Iran and their domains, components, and indicators that can be used to measure the well-being of children in Iran.
Methods: In this Delphi study, 30 experts that had educational, research, and executive experiences in various areas of children’s life were consulted. The dimensions, components, and indicators of children’s wellbeing were extracted through a review of the literature and views of the experts and children. The Delphi method was applied in three rounds. The dimensions and components with a higher-than-average score were selected and the percentage of Delphi members’ agreement with related indicators was measured.
Results: Generally, 25 components and 110 indicators related to 7 domains (physical health, safety and risks, economical situation, family, personal and social well-being, education, housing and living conditions) were selected by Delphi members. Consensus on the relevancy of indicators, proportionality, and comprehensiveness was 100%, 95%, and 86%, respectively.
Conclusion: The components and indicators suggested in this study can help to create a composite index for monitoring and comparing the status of the children’s wellbeing between different provinces of Iran in different times. It can also show the strengths and weaknesses of the policies and programs related to children’s wellbeing and help the government to adopt appropriate policies for the whole country as well as each province.
