No logical ordering for observations within a cluster-usually appropriate for data that are clustered within a subject but are not time-series data Within-subject correlations are set as an exponential function of this lag period- determined by researcherД®xchangeable-within-subject observations are equally correlated Incorrect specification can affect efficiency of the parameter estimates.ДЄutoregressive Correlation Structure- data that are correlated within clusters over time The goal of specifying a working correlation structure is to estimate B more efficiently. NOTE: The regression coefficients that results from GEE models for logit, probit, and log links need to be exponentiated before they are meaningful Regression coefficients are the expected change in the log of the mean of the dependent variable for each change in a covariateДЄlso probit link for cumulative predictive analysis of binary or ordered dependent variables and cumulative logit for ordered multinominal data No transformation of u before construction of the matrixДЄllows regression equation to map interval from 0 to 1 There are three main types of link and variance functions: The explanatory variables, X=(X1, X2, X3,вЂ¦,Xk), can be discrete, continuous, or a combination. Each yiВ can be a binomial or a multinomial response. YijВ represents the response for each subject,В i, measured at different time points (j=1,2,вЂ¦,ni). The response variable (Y) can be either categorical or continuous. GEE estimates are the same as Ordinary Least Squares (OLS) if the dependent variable is normally distributed and no correlation within responses are assumed The goal is to make inferences about the population when accounting for the within-subject correlationДЇor every one-unit increase in a covariate across the population, GEE tells us how much the average response would change Instead of attempting to model the within-subject covariance structure, GEE models the average response The GEE method was developed by Liang and Zeger (1986) in order to produce regression estimates when analyzing repeated measures with non-normal response variables.Д¬an be thought of as an extension of generalized linear models (GLM) to longitudinal data GEE can take into account the correlation of within-subject data (longitudinal studies) and other studies in which data are clustered within subgroups.ДЇailure to take into account correlation would lead to the regression estimates (Bs) being less efficient- meaning they would be more widely scattered around the true population value. Spatially correlated data-В replace time with one or more spatial dimensions Longitudinal Data-В weight taken repeatedly over time on the same individuals Multivariate Data-В a persons weight and height simultaneously measuredД¬lustered Data- weight for all members in various families is there a diet by time interaction? The covariance structure of the observed data is what makes repeated measures data unique-the data from the same subject may be correlated and the correlation should be modeled if it exists. In this study we would be interested in how the weight and diet changes over time, i.e. The treatment is diet type and is the between-subjects factor. Each man is assigned a different diet and the men are weighed weekly for one year. Three different types of diets are randomly assigned to a group of men. Observations from an individual tend to be correlated and the correlation must be taken into account for valid inference. Longitudinal studies are repeated measurements through time, whereas cross-sectional studies are a single outcome per individual This page looks specifically at generalized estimating equations (GEE) for repeated measures analysis and compares GEE to other methods of repeated measures. This page briefly describes repeated measures analysis and provides an annotated resource list.

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