Multivariate frailty models using survey weights with applications to twins infant mortality in Ethiopia

Abstract

Several studies have shown that twin birth contributes substantially to infant and child mortality mainly in resource-poor countries. The excess rates among twins call for research in statistical modeling to identify the main causes behind it. In studies involving multiple individuals from the same family, the fundamental independence assumption in the classical statistical modeling is not plausible. In addition, previous studies indicated that ignoring sampling weight while dealing with a dataset collected with complex survey design can introduce serious bias. This study is then aimed to fill these methodological gaps to integrate the dependence from twin birth with an advanced statistical gamma frailty model to correctly identify the determinants of infant mortality among twins in Ethiopia. We compiled all available data from the 2016 Ethiopia Demographic and Health Survey with a total of 908 children (454 pairs of twins) with survey sampling weight incorporated in the analysis. To identify predictors and to assess the presence and significance of frailty, semiparametric univariate, bivariate shared, and correlated gamma frailty models were fitted. The likelihood ratio test was employed to test the significance of frailty term in the model. We found that sex of the child, among twins birth order, preceding birth interval, and succeeding birth interval are significantly associated with twin infant mortality. The results of this study further confirmed the significance of the shared frailty term accounting for the unobserved heterogeneity.