Suppose observations and p covariates 
are available in 
groups with at most 
in each group, and that the mean and variance of each observation is thought to satisfy a generalised linear model:
but that the observations within a group are correlated. If the variables were uncorrelated the (quasi)score equations would be:
where 
is 
matrix of derivatives, 
is a diagonal 
matrix of variances and 
is the vector of the 
residuals 
.
The left-hand side of this equation has zero expectation at the true value of 
even if there is correlation within groups, so the estimates of 
will be consistent even for correlated data. In this equation the changing variance of 
is accounted for in the weighting by 
but no account is made for the correlations. The consistency results still hold if 
is replaced by a covariance matrix for group g, either prespecified or depending on a finite set of parameters. If this covariance matrix is close to the true covariance of 
then 
will be estimated more efficiently. The assumed form of 
is called the ``working covariance model''.
In order for this to be useful it is necessary to have reliable standard error estimates for the estimates 
. An argument based on the delta method (or a first-order Taylor series expansion) shows that
the so-called ``sandwich estimator'' gives asymptotically correct standard errors and various simulation studies have shown that its properties are good when M is small (5 or fewer). Less is known for large M.