Application of GEE Models for Assessing Maternal Health Complications

Bangladesh, a developing country, gained success towards the fifth-millennium development goals target of reducing its maternal mortality ratio by three quarters by 2015, but yet worked more on it for further reduction of maternal mortality. In this light, though Bangladesh is committed to the sustainable development goals target of reducing its maternal mortality ratio to be reduced from 170 to 105 per 100,000 live births, the scope of research on this issue is limited because the maternal morbidity data is scarce in Bangladesh. In this paper, the prospective data on maternal morbidity in rural Bangladesh (collected by BIRPERHT) have been employed to trace out the high-risk and life-threatening factors associated with pregnancy-related complications. The subject-specific generalized estimating equations (SS-GEE) model with random effect structure is used for multivariate binary data for the repeated observations. The findings indicate that the risk of suffering from pregnancy complications is higher for high economic status, lower age at marriage, not visited for medical check-ups, outside home workers, and having miscarriage or abortion. Comparing the SS-GEE model with other correlation structures and relative efficiency factors, the SS-GEE model with random effect structure is well fitted for the prospective repeated observation data.

Empirical Likelihood in Generalized Linear Models with Working Covariance Matrix

Empirical likelihood in generalized linear models with multivariate responses and working covariance matrix is discussed.Under the weakest assumption on eigenvalues of Fisher’s information matrix and some other regular conditions,we prove that the non-parametric Wilk’s property still holds,that is,the empirical log-likelihood ratio at the true parameter values converges to the standard chi-square distribution.Numerical simulations are given to verify our theoretical result.