Implementing weighted multiple linear regression with GEE

What is weighted GEE?

Weighted Generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal or clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale, correlation structures are estimate based on quasi- likelihood.
 

What are the weighted methods for analyzing missing data with the GEE procedure?

The GEE procedure implements two weighted methods, observation-specific and subject-specific, for estimating the regression parameter ˇ when dropouts occur. Both provide consistent estimates if the data are MAR.

What is the difference between GEE and mixed model?

Mixed effect modeling allows both fixed (aka marginal) and random effects, while GEE modeling allows for fixed effects alone. A fixed effect is akin to a population effect: some measured variable is believed to have a single effect across the population.
 

Does GEE require balanced data?

– not preferred since they require balanced and complete data sets, require normally distributed response variables and do not allow for the analysis of covariates that change over time.

What is the difference between GLMM and GEE?

Whereas the GLMM explicitly models the within-subject correlation by using random effects, the GEE implicitly accounts for such correlations by using sandwich-type variance estimates 6. Analysis of Longitudinal Data, 2, Oxford: Oxford University Press.

What is the advantage of GEE?

Gen- eralized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure.

Is GEE robust to missing data?

GEE has been shown to yield consistent estimates of the regression parameters for a marginal model when data are missing completely at random (MCAR). However, when data are missing at random (MAR), the GEE estimates may not be consistent; the MI approaches proposed in this paper minimize bias under MAR.
 

Can GEE handle missing data?

We propose two methods for handling missing data in generalized estimating equation (GEE) analyses: mean imputation and multiple imputation. Each provides valid GEE estimates when data are missing at random. Missing outcomes are imputed sequentially starting from the outcome nearest in time to the observed outcome.

How do you do multiple imputations for missing data?

Calculating Imputations

1. Fit your data to an appropriate model.
2. Estimate a missing data point using the selected model.
3. Repeat steps 1 and 2 (you can use the same model, or different models) 2-5 times for each missing data point (this gives you multiple options for the missing data).
4. Perform your data analysis.

What does a GEE measure?

In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints.

What does weights do in GLM?

Weights are used to tell your model that some observations are more important than other ones.
 

What is weighted average forecasting?

Weighted Average Forecasting is a method that determines how much inventory to keep on hand based on an item’s past performance and an assigned “weight” or emphasis. The formula works well for items that regularly sell, with sales in at least 8 of the prior 12 periods.