Proc GLM multiple comparison using Bonferroni adjustment test

[Mutua]

Dear SAS community,

I would like to do multiple means comparisons for my treatments using a one way ANOVA in Proc GLM.

I would like to separate the means using the letters but it seems using Proc GLM and Adjust=bon; cannot work. I have tried with lsmeans x /adjust=bon; and apparently that works. However, i get a the Least Squares Means table which i am not able to interpret.

I have searched for the SAS code for Bonferroni adjustment in Proc GLM, but seems i am not lucky.

Here is a protocol i am testing:

Proc GLM;

Class Patient Rep    PF    RF    RS;

model PF=Patient;

means Patient/adjust=bon ;

Run;

The underlined above does not work,

Anyone with suggestions will be highly welcome to help me out with this problem.

Best regards

[PGStats · Accepted solution]

The proper syntax is described in the documentation. Try

means Patient / bon lines;

PG

[Mutua]

Dear PGStats,

Many thanks for the feedback on what i should try with the Bonferroni adjustment. I have a new question. I do know that Bonferroni is a very conservative method of means comparisons. I intend to submit my research manuscript to a journal, who have indicated that use of Tukey's as a multiple means comparison procedure is prohibited.

I conducted an experiment with ten levels of treatments and three response variables in a completely randomized design. However, there are suggestions from people who have pre-reviewed my draft manuscript that i should change analysis to Bonferroni adjustment. Some of the data i have are counts which i have done log transformation. I used Proc GLM for the analyis. However after the Bonferroni adjustments, i cannot find significant differences among my variables (e.g a mean of 20.7 is not different from that of zero mean (0).

I need to find a better way of doing multiple comparisons among my ten levels (varieties) replicated five times. Which is the best adjustment method that is not very conservative and suitable for this kind of analysis?

Your support will be highly appreciated.

Regards

[PGStats]

Please post this new question to the SAS Statistical Procedures community. That is where you will likely get the best advice. - PG