bear v1.0.0 - a data analytical tool for ABE in R [🇷 for BE/BA]

posted by ElMaestro  – Denmark, 2008-07-24 12:04 (6192 d 16:13 ago) – Posting: # 2076
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(edited on 2008-07-24 12:18)

Dear Yung-jin,

I don't think the software should state it uses GLM if it doesn't. But that's of course only a personal view and I mean no offense at all.

Regarding proc mixed and proc GLM in SAS my impression is* that for balanced data they achieve the same thing, but proc mixed is more computationally intensive. Therefore, in the old days when computers were slow GLM was preferred. Nowadays, when computational power is not an issue it makes little difference.
For data without balance, however, proc mixed is used and proc GLM may be unsafe. All in all, one can say it is always kind of safe to use proc mixed.

Thus, I'd suggest you to use a mixed model the following way:
"lmeYungjin <- LnAUC0INF ~ seq + prd + drug, random=~1|subj, method="REML")
From the lmeYungjin fit you can extract the correct sigma (which is correct whether or not you have balance) and the treatment difference and use this to construct the 90% CI.

Next, you can do a glm as follows:
"glmYungjin <- glm(LnAUC0INF ~ seq + prd + drug + subj)"
followed by "drop1(glmYungjin, test="F") to get a glm anova with type III SS.

Last you just need to update the anova with the correct error term for the sequence effect (between subj error).

Result: Anova and 90% CI done the way SASoholics like and it is resistant to imbalance....

Best regards
EM


*: When I say "my impression is" then it of course means I could be completely wrong and I'd be happy to stand corrected accordingly.
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