Covariance pattern models versus generalized estimating equation models

Question

Can somebody please explain the major differences between covariance pattern models (Hedeker and Gibbons, Chapter 6, 2006; Jennrich and Schluchter 1986) and generalized estimating equation models (Hardin and Hilbe, 2012; Liang and Zeger, 1986).

My presumption is that the main difference is around ML/REML estimation in the covariance pattern models case; versus, quasi likelihood estimation in the GEE case. Is this correct?

And also, the applicability of covariance pattern models to Gaussian response data whereas, GEE is more applicable to response data of many other distributions (Gaussian, Binary, Binomial, Poisson, etc).

Are covariance pattern models with Gaussian response and some identity link and GEE (identity link, Gaussian response) similar/identical?

Not exactly what you are looking for, but it may help you to read my answer here: Dfference between generalized linear models generalized linear mixed models in SPSS. — gung - Reinstate Monica, Jan 21 '14 at 17:55

score 5 · Accepted Answer · edited Apr 20 '22 at 00:32

Actually you have correctly listed the major differences between the covariance pattern models and GEE models. One thing I would like to add is that, for the Section 6.2.5 "Random Effects Structure" of Hedeker and Gibbons (2006), the two models would be characterized as subject-specific (conditional) models and population average (marginal) models respectively, though the two coincide for linear cases. See my answer here: What is a difference between random effects-, fixed effects- and marginal model?

I would say the two are numerically equivalent, though they use different estimation methods. See the example in Stata below. Note that the covariance pattern models can be fitted with command mixed, but the random effects are suppressed by the option noconstant. Of course, we can turn to REML instead of ML to obtain unbiased variance estimates.

. webuse pig
. mixed weight week || id:, noconstant 
residuals(exchangeable)
Mixed-effects ML regression                     Number of obs      =       432
Group variable: id                              Number of groups   =        48
                                            Obs per group: min =         9
                                                           avg =       9.0
                                                           max =         9

                                            Wald chi2(1)       =  25337.48

Log likelihood = -1014.9268                     Prob > chi2        =    0.0000

  weight |      Coef.   Std. Err.      z    P&gt;|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------
        week |   6.209896   .0390124   159.18   0.000     6.133433    6.286359
       _cons |   19.35561   .5974056    32.40   0.000     18.18472    20.52651

. xtset id week
. xtgee weight week, corr(exchangeable)
GEE population-averaged model                   Number of obs      =       432
Group variable:                         id      Number of groups   =        48
Link:                             identity      Obs per group: min =         9
Family:                           Gaussian                     avg =       9.0
Correlation:                  exchangeable                     max =         9
                                                Wald chi2(1)       =  25337.48
Scale parameter:                  19.20076      Prob > chi2        =    0.0000

  weight |      Coef.   Std. Err.      z    P&gt;|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------
        week |   6.209896   .0390124   159.18   0.000     6.133433    6.286359
       _cons |   19.35561   .5974055    32.40   0.000     18.18472    20.52651

But in GEE, we often use robust (empirically) standard errors instead of model-based standard errors. When we add the option robust, only the standard errors change.

. xtgee weight week, corr(exchangeable) robust
GEE population-averaged model                   Number of obs      =       432
Group variable:                         id      Number of groups   =        48
Link:                             identity      Obs per group: min =         9
Family:                           Gaussian                     avg =       9.0
Correlation:                  exchangeable                     max =         9
                                                Wald chi2(1)       =   4552.32
Scale parameter:                  19.20076      Prob > chi2        =    0.0000
                                 (Std. Err. adjusted for clustering on id)


         |               Robust
  weight |      Coef.   Std. Err.      z    P&gt;|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------
        week |   6.209896   .0920382    67.47   0.000     6.029504    6.390287
       _cons |   19.35561   .4038676    47.93   0.000     18.56405    20.14718

Covariance pattern models versus generalized estimating equation models

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