Selecting Covariance Structure to Analyze Longitudinal Data: A Study to Model the Body Mass Index of Primary School-Going Children in Bangladesh

In the longitudinal study, the data are collected from the same subject over time and hence the data are correlated. To analyze such data selecting an efficient covariance structure is very important to get better results. Therefore, this article is aimed to select an efficient covariance structure to model the body mass index (BMI) of primary school-going children in Bangladesh. In this study, at first, we have conducted a longitudinal survey to build a cohort of 100 primary school-going children in Sylhet city, Bangladesh. We collected the information from the same children at the initial time (T₀), after 6 months (T₆), after 12 months (T₁₂) and after 18 months (T₁₈). Linear mixed model (LMM) is applied for selecting an efficient covariance structure and then to model the body mass index. To find out a better covariance structure, we used diagonal, unstructured (UN), auto Regressive order 1 (AR1) and compound symmetry (CS) covariance structures in collecting longitudinal data. Observing all the criteria, it is found that the covariance structure compound symmetry (‘CS’) gives better results for LMM. Finally using the CS covariance structure, initially, we observed that the BMI of male students’ is comparatively smaller than female students’ (Estimate = -−.04, P-value = 0.03). But overtime, a reverse result is observed at T12 and T18. Taken together, we may conclude that compound symmetry (CS) gives better output to model the body mass index of primary school-going children. As female students are getting more obese, in addition, today’s female children are the mothers of the future. Therefore, parents should give concentration to female children to reduce their body weight. This study may be useful for researchers in public health sectors to select a proper covariance structure to analyze their longitudinal data.