Questions tagged [gee]

Stands for Generalized Estimating Equations which is an approach to estimating regression coefficients. GEE can be used on clustered / longitudinal data and has the attractive property that it provides consistent estimators of regression coefficients and unbiased inference even when the association structure within a cluster is mis-specified.

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35
votes
3answers
48k views

Difference between generalized linear models & generalized linear mixed models

I am wondering what the differences are between mixed and unmixed GLMs. For instance, in SPSS the drop down menu allows users to fit either: ...
65
votes
3answers
71k views

When to use generalized estimating equations vs. mixed effects models?

I have been quite happily using mixed effects models for a while now with longitudinal data. I wish I could fit AR relationships in lmer (I think I'm right that I can't do this?) but I don't think it'...
28
votes
1answer
22k views

What is the difference between generalized estimating equations and GLMM?

I'm running a GEE on 3-level unbalanced data, using a logit link. How does this differ (in terms of the conclusions I can draw and the meaning of the coefficients) from a GLM with mixed effects (GLMM)...
5
votes
2answers
4k views

Conditional vs. Marginal models

I have data with an outcome of 0 or 1 (binary) representing success or failure. I also have two comparison groups (Treatment vs. Control). Each subject in the study contributed 2 observations (the ...
12
votes
1answer
12k views

Marginal model versus random-effects model – how to choose between them? An advice for a layman

In searching for any info about marginal model and random-effects model, and how to choose between them, I have found some info but it was more-or-less mathematical abstract explanation (like for ...
4
votes
2answers
5k views

How can I estimate model predicted means (a.k.a. marginal means, lsmeans, or EM means) from a GEE model fitted in R?

I am trying to obtain model-predicted means and CI's for a categorical predictor in a GEE model fitted with the geeglm function (geepack package). The model is fitted with no problem, but where I am ...
2
votes
1answer
4k views

GEE, quasi-likelihood and what it generalizes

Wikipedia formulates Generalized Estimating Equations (GEE) as Given a mean model, $\mu_{ij}$, and variance structure, $V_{i}$, the estimating equation is formed via: $$ U(\beta) = \sum_{i=...
2
votes
1answer
7k views

Getting the variance-covariance matrix of regression coefficients in GEE

I fitted a GEE model using the function genZcor with user defined correlation matrix. I want to get the var-cov matrix of the regression coefficients. But the ...
8
votes
1answer
861 views

Models for Generalized Estimating Equation?

From Wikipedia, Generalized Estimating Equation (GEE) is a method to estimate the parameters of a generalized linear model (with an exponential family distribution for the response). By reading other ...
1
vote
2answers
9k views

Interpretation of GEE coefficients

Suppose blood pressure is a continuous outcome variable and you run a linear GEE with following predictors: age (years), weight (lbs), and smoking (yes/no). How would you interpret the coefficients ...
2
votes
2answers
7k views

How to interpret regression coefficients for a variable with takes positive and negative values?

I am running a GEE negative binomial regression to see how predictors affect the onset of violence through time. I have an $X$ variable (vegetation cover) which is calculated as whether an ...
0
votes
1answer
182 views

How to analyze data with more than one associated categorical dependent variables?

I have some dependent variables related to the growth of a company having categories like (e.g. for variables indicating net profit, financial turnover etc.) (1) decreasing, (2) stable, (3) ...
0
votes
0answers
386 views

Effective sample size reduction for correlated data analyses

In power and sample size calculations, and minimum detectable effect analyses for GEE, I have often seen an effective sample size reduction used to account for correlation. The idea is that the more ...
6
votes
1answer
6k views

GEE with exchangeable working covariance vs. assuming independence and using Huber-White standard errors?

I'm analyzing a dataset including 13000 students. Students are clustered into schools/grades. The ICC (intraclass correlation coefficient) shows that students in a same school are correlated. ...
5
votes
0answers
1k views

Diagnostics for GEE in R

I have been checking out which diagnostics to use for a GEE analysis. It seem that influence measures are appropriate (Preisser, 1996). Does anyone know of a package that can be used in R to examine ...
4
votes
1answer
3k views

How many clusters for linear mixed models and GEE?

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest ...
9
votes
3answers
8k views

Friedman test and post-hoc test for Python

In my dataset, I have five (ordinal) groups with an x-amount of measurement. Because homoscedasticity is violated, I performed the Friedman chi-square test to see if there are any statistical ...
8
votes
1answer
937 views

How to analyze GEE with unevenly spaced observations?

I am interested in using Generalized Estimating Equations (GEE) to model longitudinal count data. I recorded animal count observations on the same sites on many days but the spacing of the ...
9
votes
1answer
17k views

What is the difference between GLM and GEE?

Whats the difference between a GLM model (logistic regression) with a binary response variable which includes subject and time as covariates and the analogous GEE model which takes into account ...
7
votes
2answers
954 views

What's the difference between estimating equations and method of moments estimators?

From my understanding, both are estimators that are based on first providing an unbiased statistic $T(X)$ and obtaining the root to the equation: $$c(X) \left( T(X) - E(T(X)) \right) = 0$$ Secondly ...
2
votes
1answer
417 views

Statistical Power of ROC/AUC Test with non-IID Samples :: To how many IID Samples are my non-IID Samples Equivalent?

I've been assigned to solve the following problem as part of a serious, biological research project. I think I have a tentative solution, but I'm wondering whether the approach I've picked is the best....
1
vote
1answer
50 views

Interpretation of mixed-effects versus GEE poisson/logit regression parameters

What's the difference in the interpretation of the model parameters (intercept +slope) in the mixed-effects model and GEE model for poisson and logistic regression?
1
vote
1answer
2k views

QIC or QICu for variable selection in GEE-GLM variable selection

I'm fitting a binomial GEE glm to predict presence or absence of sperm whales as a function of environmental variables. I am using latitude, longitude, depth, and distance to land, and year as a ...
5
votes
1answer
2k views

Interpreting coefficients of ordinal logistic regression when there is clustering within the data

I have built and refined a regression model using the ordinal package in R. The measure is $0>1>2>3>4>5$ (Yes/No ...
2
votes
1answer
1k views

Mixed Model Versus GEE estimates and which to use

I am new to both GEE and mixed modeling, so please bare with me: Briefly: my exposure is television viewing in childhood (tv) and I am trying to assess change in body mass index (bmisds) over 3 time (...
2
votes
1answer
294 views

Interpreting ordinal GEE coefficients

I have a dataset with an ordinal dependent variable (iws_w) with a range of -3 to +1. I placed it, with two independent variables in an Ordinal Generalized ...
0
votes
0answers
59 views

Does this analysis make sense and can anyone provide relevent references?

I have a GEE logistic regression model that I've built that predicts that probability of mortality given a rather extensive set of covariates. I wanted to write here as a sanity check of sorts to ...
5
votes
1answer
8k views

Generalized estimating equations output in SPSS

I am hoping to confirm that I have a suitable way to analyse the different proportions of people who are categorized as left lateralised on the one hand, or bilateral/right lateralised on the other in ...
4
votes
1answer
1k views

Does the sandwich estimator in GEE protect against both correlation misspecification and heteroscedasticity?

The relative merits of GEE with exchangeable correlation or GEE with independence and the sandwich estimate have been discussed, but I couldn't find a post specifically addressing my question. I have ...
3
votes
1answer
86 views

What does it mean exactly to have marginal (population-averaged) or subject-specific effects, say in mixed models vs. GEE?

I know that mixed-effect models produce subject-specific answers and the GEE produces marginal effect. But I don't get it very well still, I'm sorry for a silly question. Let me explain what exactly ...
1
vote
2answers
837 views

GEE vs LME, non-normal distribution

I have a question about statistics theory. I have longitudinal, repeated measures data where the response variable is skewed right. Using R, I ran a linear mixed-effects model (good for longitudinal, ...
1
vote
1answer
2k views

Interpreting a longitudinal generalized estimating equations beta cofficients

I've been struggling with wrapping my head around the GEE beta coefficients and I don't think I fully get it. There are other questions on CrossValidated that ask about GEE in the binary context (...