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I'm attempting to fit a GEE model and I have a question about using the AR(1) working correlation matrix. I've read some conflicting information about this correlation matrix. In some books and articles, I've seen that AR(1) should only be used when the measurements are made at equally spaced intervals, while in other articles, I've seen that this is not necessarily so. I have a population of patients with varying hospitals vists. Each of the patients in my study visit the hospital at various times and at different intervals. Given this, can it be appropriate to use an AR(1) working correlation matrix for this data? Does it really matter or make a big difference if I'm using the sandwich variance estimator and have about 90,000 patients/clusters?

Thanks.

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When using a GEE with the sandwich variance estimator, the working covariance matrix is merely for more efficient estimation; i.e. if your working covariance matrix is correct, then you will have optimal estimates.

If you "miss" with the working covariance matrix, but still use the sandwich variance estimator, you will still have valid inference, although your standard errors may be larger than they could have been had you chosen a more appropriate working covariance matrix.

So given that you have quite a bit of data, even if AR(1) were to be a very bad fit, you have very little worry about; your standard errors will likely still be very small anyways, so the cost of using the wrong covariance structure should be minimal.

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