Comparison of marginal and mixed models in medical data analysis

In medical researchers, there are lots of correlated data which cannot be analyzed using the usual classical statistical methods because the assumption of independency between observations is not met. Data from cluster sampling, longitudinal studies, observations on paired organs and matched studies are examples of such data. Two statistical methods that can be used to correctly handle these kinds of data are marginal and mixed models. These models are different in considering correlation between subjects and Interpretation of the regression coefficients. The regression coefficients in marginal models with non-identity link functions show the change in population whereas in mixed models they represent changes within a subject or a cluster. In result, in nonlinear models, application of these two kinds of models depends on the areas of their usage. While the marginal models are more attractive to the Health policy makers who are considering the potential effects of a variable on the population as a whole, the mixed model will be of most interest to a physician in a physician/patients context.