Abstract
Joint modelling is a statistical approach that is used to analyze correlated data when two or more outcome variables are correlated. By joint modeling, we refer to the simultaneous analysis of two or more different response variables from the same individual. But in a separate model, it is unable to measure the effect of covariate simultaneously. This article focuses on separate and joint modelling for correlated discrete data, including logistic regression models for binary outcomes. Since most of the women are illiterate in Bangladesh and most of the people are living in urban areas, as a result, most of the women are not aware of immunization. But an educated mother is always aware of her child's health which is dependent on immunization. Therefore, mother education and immunization are interdependent. We jointly address the effect of maternal education and immunization. Joint modeling of these two outcomes is appropriate because mother education helps raise awareness of the child's health and immunization is the prevention of various diseases for the child's health. We also identified factors influencing maternal education and immunization among women in Bangladesh. By jointly modelling we found the correlation between maternal education and immunization and the most important contributing factor. The joint model removes a less significant impact of covariates as opposed to separate models. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed between different responses, which is overestimated or underestimated when examined separately.
Highlights
Mother education plays an important role in determining the health conditions for children
We used hierarchical logistic regression to validate the presence of variation within an individual
Based on the findings presented joint models deflate parameters that are otherwise overestimated in separate models
Summary
Mother education plays an important role in determining the health conditions for children. Most studies have ignored the interdependence between these two related outcomes and considered them separate events, leading to misleading results due to the obvious relationship between them. Both mother education and immunization are accounted for the overlapping effects from one event to the other. Our study fits two sets of models, including a separate model and a joint model. Each of these hierarchical logistic regressions addresses intraclass correlation. We shared 2014 Bangladesh Demographic and Health Information as a tool to address mother education and immunization. We provided a clear evaluation of the advantage of joint modelling over separate modelling and compared the coefficients in both model estimates
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