Multiple logistic regression in statplus1/22/2024 ![]() ![]() GEE allows us to specify a correlation structure for different responses within a subject or group.Because GEE doesn’t use likelihood methods, the estimated “model” is incomplete and not suitable for simulation.GEE does not use the likelihood methods that mixed-effect models employ, which means GEE can sometimes estimate more complex models. GEE computations are usually easier than mixed-effect model computations.This is something better suited for a mixed-effect model. It cannot easily accommodate more complex designs such as nested or crossed groups for example, nested repeated measures within a subject or group. GEE is intended for simple clustering or repeated measures.We can also obtain a population-level model from a mixed-effect model, but it’s basically an average of the subject-specific models. ![]() This in turn provides insight to the variability between subjects or clusters. In other words, the parameter estimates are conditional on the subject/cluster. They allow us to estimate different parameters for each subject or cluster. Mixed-effect/multilevel models are subject-specific, or conditional, models. The main difference is that it’s a marginal model.We often model longitudinal or clustered data with mixed-effect or multilevel models. In this article we simply aim to get you started with implementing and interpreting GEE using the R statistical computing environment. If interested, see Agresti (2002) for the computational details. The name refers to a set of equations that are solved to obtain parameter estimates (i.e., model coefficients). It is usually used with non-normal data such as binary or count data. Generalized estimating equations, or GEE, is a method for modeling longitudinal or clustered data. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |