Thanks Kurt, it will not be that easy to simulate a 'nice' distribution over the levels as only 11 out of 288 records are printed. I think the data originate from: Journal of Cardiovascular Pharmacology. 18:S35-S38, 1991. https://journals.lww.com/cardiovascularpharm/toc/1991/06184 Any other 3-level dataset is welcome as well as of course!
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I am in search of the data underlying the 3-level model (mctrial dataset, page 6). I would like to play around with several covariance structures and also use it as an example in our courses on quantitative veterinary epidemiology (where we exclusively use SAS). Unfortunately the email address of the author of this paper does not exist anymore. Hope somebody can help! Kind regards, Klaas
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Thanks! One can specify TECH=Newton as option in proc logistic's model statement, but it gives the same output as Fisher scoring. So, you really need 'Newton with ridging' to come to identical estimates. Good to know when small frequencies are involved!
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And this is the proc genmod: proc genmod data=check descending; class factor/param=ref ref=first; model event=factor/dist=bin link=logit; estimate 'OR' factor 1/exp; run;
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I wonder why the odds ratio estimated by proc logistic differs from the one obtained from proc freq and proc genmod in the example below (SAS 9.4 TS Level 1M5). The OR's become identical when the low frequency event=0/factor=1 is set to above 20. Using logistic regression in STATA on this data gives the same OR as the one from proc freq and proc genmod. I would like to know why proc logistic gives a slightly different OR (I know exact logistic regression should be used on sparse data). data check; do i=1 to 52; event=1; factor=1; output; end; do i=1 to 287; event=1; factor=0; output; end; do i=1 to 1; event=0; factor=1; output; end; do i=1 to 385; event=0; factor=0; output; end; run; proc logistic data=check descending; class factor/param=ref ref=first; model event=factor; run; proc freq data=check; tables event*factor/cmh; run;
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