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Posted 02-10-2020 10:29 AM
(1287 views)

Hello,

I am conducting a repeated measures analysis with a normally distributed dependent variable. I have 3 time points, but my data are imbalanced and not all observations have 3 time points. I have roughly 8k subjects. As a result, I chose to use proc genmod to conduct a regression. My understanding with proc genmod is that it produces estimates that are robust to the covariance structure. However, with my data, specifying different covariance structures produces substantially different coefficient estimates and standard errors/p-values. If genmod is supposed to produce robust estimates, why would the regression results change dramatically?

```
/*unstructured matrix*/
proc genmod data=dat;
class id time(ref = "0");
model y = x x_2 time /dist = normal;
repeated subject = id/type = un corrw covb;
run;
/*independent matrix*/
proc genmod data=dat;
class id time(ref = "0");
model y = x x_2 time /dist = normal;
repeated subject = id;
run;
/*autoregressive matrix*/
proc genmod data=dat;
class id time(ref = "0");
model y = x x_2 time /dist = normal;
repeated subject = id/type = ar(1);
run;
```

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4 REPLIES 4

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It is Mixed Model thing.

Different assumption for different covariance structures, produce different estimated coefficient.

@Rick_SAS wrote some blog about it recently.

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Thank you @Ksharp

I found this blog post by @Rick_SAS about longitudinal mixed effects models. However, it still doesn't explain why the esimates would vary dramatically according to correlation structure...especially with proc genmod which should be robust to misspecifying the correlation structure.

Do you know of any other resources that I could look to?

Thanks in advance.

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I don't feel it is unexpected that changing the correlation structure can have big impacts on the estimates of the parameters.

This paper gives advice on how to choose the proper correlation structure: https://support.sas.com/resources/papers/proceedings/proceedings/sugi30/198-30.pdf

--

Paige Miller

Paige Miller

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