Questions tagged [generalized-estimating-equations]

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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If I analyse the pre-post data, should the "pre" (time=0) be included or excluded from the reponse?

Let's assume I analyse repeated data, recorded at t0, t1...t3. I want to analyse the response itself and then check various contrasts, for example change from baseline or consecutive. If the model is: ...
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Empirical risk minimization for relu/max loss function

Classical risk minimization (RM) minimizes the expected loss over the training distribution $p_{\mathrm{train}}(x)$, $$\theta^*_{RM} = \arg \min_\theta E_{p_{\text{train}}}[\ell(x, \theta)].$$ As the ...
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If the normality assumption in the for the GLS estimation fails, would you switch to GEE?

I want a marginal model, ideally fit via GLS. But the normality of residuals doesn't hold. It isn't much skewed, I don't want any transformations. It's just non-normal in shape. Yet still reporting ...
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Why is the baseline x time interaction included in clinical trials models (MMRM)?

I noticed, that many, many times, when the longitudinal studies are analysed for efficacy, the following components are taken as fixed effects: time treatment time : treatment (interaction) baseline ...
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Is the Generalized Estimating Equtions method a good non-parametric replacement for the Generalized Least Squares?

I want to use GLS on my longitudinal data, but it turns out that residuals are non-normal and is a non-easy way. Not just "transformable" skewness, no known relatioship between mean and ...
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In a typical clinical longitudinal trial, why do marginal models (MMRM, GEE) are much more common than conditional ones (mixed)?

I read statistical analysis plans and reports of over 80 longitudinal trials. I noticed, that in 70% they were analysed using a marginal model, namely the MMRM approach (mixed-model repeated measures)....
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Will I be likely criticized, if use the independence covariance structure for the GEE method?

It can be read, that the GEE method is robust to covariance structure misspecification. One can choose even the... independence structure for repeated observations, and still it may work. I understand,...
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Why do some poeple claim, that choice of the working correlation in GEE doesn't affect the marginal coefficients?

I found this discussion: GEE: choosing proper working correlation structure Cite: Correlation structure in GEE, unlike mixed models, does not affect the marginal parameter estimates (which you are ...
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Is GLS really a special case of the GEE?

I was told, that the GLS is a special case of the GEE, if the conditional distribution is gaussian and the link is identity. How is that possible? GLS is a two (or more - IWLS) stage procedure. It ...
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What is the purpose to have the "independent" covariance structure in GEE or GLS?

The methods of estimation like GLS or GEE are especially helpful, when there are clusters of data, like repeated observations, many per cluster=subject. Such observations are naturally correlated in ...
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Why is anova not significant when analysis of the str error multiple regression model suggests that it is? [duplicate]

I'm a bit confused as the output of my model in R. I have built a generalised estimating equasion glm model aiming to see the effect of time (here coded as timestrat) on a variable called new1804. I ...
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Does the GEE estimation and mixed models handle data missing inside a series?

I'm wondering, if either GEE or mixed models can handle all the 4 cases of missing data: T0 T1 T2 T3 m + + + (missing at t0, late entry) m + + (missing inside a series, here at t1) m (missing ...
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If "conditional to random effect" in mixed models a kind of averaging or calculated differently?

I read practically every discussion in this forum, and still I don't get it. I'm sorry, but all the explanations still don't tell me how to interpret it. Please don't cite other articles, as I saw all ...
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Why is it said, that GEE is not likelihood based, IF the estimating equations are derived exactly thru d(log(L)/dB?

I found this article: https://sakai.unc.edu/access/content/group/2842013b-58f5-4453-aa8d-3e01bacbfc3d/public/Ecol562_Spring2012/docs/lectures/lecture22.htm#choosing and most of it is showing the ...
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How is that possible that SAS and R can test for main and interaction effects for the GEE if it has no likelihood?

I was taught, that GEE, being not likelihood based, has no way to compare models. That we cannot assess the main and interaction effects the way we do with ordinary GLM, OLS, GLS, mixed models and so ...
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correlation matrix from geeglm function

I'm using geepack::geeglm() to fit a gee model. I can't figure out how to get the correlation matrix from the output though it does not show up when ...
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Show Cox regression estimating equation is unbiased

Let $N(x) = I(X \leq x, \Delta=1)$ be the counting process for observed failure events, where $X = \min\{T,C\}$ and $\Delta = I(T \leq C)$, for censoring time $C$ and failure time $T$. Assume that $T$ ...
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Which test or method can I use to assess trend in dependent proportions over time?

Let's assume there are 4 time points, t0 (baseline) ... t3 At each time point I assess a % of successes of some outcome: p0, p1...p3. The denominators vary over time, so the counts itself do not ...
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the coefficient of a variable is the average of the coefficients of its two parts in generalised estimating equation models

I used the z-score of each variable in generalized estimating equation models. one independent variable A was divided into two parts, and the z-score of each part was taken as an independent variable. ...
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GEE - ROC Curves the Same Using Different Cluster Variables

I am creating multiple GEEs with the same covariates, but I am testing different clustering variables. The outcome is a binary yes/no variable and both VAR1 and VAR2 are binary, as well. Patients can ...
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GEE Explained Variance is Negative

I am creating a series of GEE models and a couple of statistics I normally report are the ICC and the explained variance for both Level 1 and Level 2 of the model. However, I am calculating that for ...
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Multivariate Regression with Two Different Types of Response

Problem Setting: I have an interesting question related with longitudinal study and multivariate regression. I found that in lots of biomedical studies, multiple discrete and continuous endpoints are ...
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Inferring effect & effect modification from simulation data

I have a "black box" system (computer simulation), which takes inputs: $x_1 \in [0,1]$, $x_2 \in [0,1]$, and $N_i$ others $\vec\theta = \{\theta_1, \dots, \theta_{N_i}\}$, and produces an ...
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How to get an overall p-value for an independent categorical variable using generalized estimating equations (geeglm) in R

I would like to know if there is a way to get an overall p-value for an independent categorical variable using generalized estimating equations in R. When I run the analysis, the output provides p-...
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difference between standard error and robust standard error

What is the difference between standard error vs robust standard error? How is the robust standard error calculated in the Generalized Estimating Equation context?
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GEE working correlation

I've been fitting a GEE on a longitudinal study with a complete case analyis. First problem arises that every working correlation gives the same standard errors and convergence is reached after 1 ...
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Marginal model of longitudinal data with missing data

I have longitudinal dataset with measurements taken over years. I plan on modelling a marginal model with a binary outcome (0/1). My plan was using a GEE for this. This binary outcome has quite a ...
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Generalised linear modelling data layout

I am working with choice test data from an Ecological study. I have multiple tests, where individuals were given the choice in each experiment to choose one of two options (control or treatment/ 0 or ...
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GEE vs Hierarchical linear regression

I am trying to choose between GEE and hierarchical linear regression for analysis of experimental vignette (2x2 factorial (0/1) design) data. Each respondent (N=160) filled in 2 vignettes, thus the ...
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Analysis of cross-sectional data with few clusters

I would like to analyze cross-sectional data of 300 patients. I would like to compare a continuous dependent variable between 7 different patient groups. The data is clustered because the patients are ...
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Why is it that GEE assumes MCAR while likelihood-based methods only assume MAR

I often hear that GEE has a very strong assumption that the data are missing completely at random (MCAR). On the other hand likelihood-based methods such as mixed models only assume missing at random (...
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Do the robust standard errors in GEE also protect you from a skewed errors

I have a numeric outcome with repeated measures on individuals and several predictors. The basic analysis is linear regression, but with consideration of random effects or GEE to adjust for the ...
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How to calculate standard deviation for estimated marginal means calculated after a GEE model in R

I have an unbalanced longitudinal dataset that looks at the effects of diagnostic tests on costs over time. First, I run a GEE using geeglm from geepack package ...
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using survival::pspline in GEE model in R

If I want to use a penalised spline basis for a predictor in a GEE model using R, is it possible to simply use the pspline function from the survival package? For example, ...
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Matched Pairs, GEE Models, and Other Regression Models

I am presenting the following hypothetical example in which the variables may or may not make sense clinically. A study has 100 matched pairs. A matched pair, in the study’s context, is defined as a ...
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GEE (generalized estimating equations) or GLMM (mixed effects models)

I have a repeated measurement data, The observations are single Bernoulli trials about students. Each student has 4 observations, students are grouped clustered into classes and schools. I want to ...
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Is our GEE model right approach here?

I took a couple of classes in basic statistics but I have bare minimum knowledge of advanced regression modeling I worked on a research project where we had a binary outcome Yes/No(visit) looking at ...
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How can I use GAM for factor variables?

I have factor variables like season (Summer and Winter), Time of Day (Morning, Afternoon, Late morning and Evening)and Landscape feature (Agriculture, Grassland etc._. I want to see the effect of ...
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Could we consider the cell experiment at multiply time points as a repeated measurement design?

We conduct a study for exploring the mechanism of osteoporosis. There is an experiment about observing the biological reactions of osteocyte when we treat it as three drugs (A, B, C type). First, we ...
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Comparing AIC and QIC (GEE and GLM)

I'm doing an analysis of expression data with 5 timepoints and a binary treatment variable. It is common not to use GEEs of any kind in this field and timepoints are often modeled using model.matrix(),...
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Quasi-likelihood and matrix dimension

Quasi-likelihood estimating equations (quasi-score function) for the estimation is as follows $$\sum_i\frac{\partial{\mu_i^{'}}}{\partial{\beta}}V_i^{-1}(y_i-\mu_i)=0.$$ The $\frac{\partial{\mu_i^{'}}}...
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Are the Estimates in geeglm standardized coefficients? How to check or get them in a longitudinal model?

I am performing a longitudinal data analysis with the generalized estimating equation and I've been asked to confirm whether the Estimates from geepack::geeglm ...
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Interpreting output from a SPSS generalized estimating equations analysis

I am trying to interpret the table shown below, which is an output of an SPSS generalized estimating equation calculation to calculate the odds ratio of individuals in groups A (Girls) and B (Boys) to ...
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The method for analyzing the repeated measures study when the data was non-normal distributions

When we conduct the repeated measures data (a continuous dependent variable) by using the method of repeated measures ANOVA, GEE or Multilevel models, the data was need follow normally distributed (...
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Can you write the correlation matrix of AR-2 (or AR-p)?

It is easy to write the autoregressive of order 1 (AR-1) correlation matrix: $$R_{k-1}=\begin{pmatrix} 1 &\rho &\rho^2 &\cdots &\rho^{k-2} \\ \rho &1 &\rho &...
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How to analyze the data for multiply measuring result from the mice pass the machine for Gait Analysis?

We want to compare the difference of the data from Gait Analysis between normal group and treatment group in mice. There are 6 mice in the normal group and treatment group, respectively. We plan to ...
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Actor Partner Interdependence Model (APIM) using Generalized Estimating Equations (GEE) in R

I am trying to analyse an APIM using GEE in R because all of my variables are binary. If using Lavaan::sem(), the code would look something like this: ...
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Can GEEs handle the Simpson's paradox?

I'm a somewhat seasoned user of mixed effects/multilevel models but I don't know much about Generalized Estimating Equations (GEEs). Over time, I've been seen many people claim that GEEs can be used ...
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In Twisk (2013) on the equation describing a generalized estimating equation (GEE), why is there a subscripted '1' after beta?

In his book on Applied Longitudinal Data Analysis for Epidemiology, page 60 there is an equation that describes a generalized estimating equations (GEE) model. " This equation models the ...
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Do GEE estimator properties (e.g. bias) change depending on the correlation structure?

A logistic regression estimated using a generalized estimating equation with a clustering variable and an independent correlation structure produces the same parameter estimates as an MLE logistic ...
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