SAS Support Communities

Joka · ‎05-15-2014

Hi, Can using multiple comparison procedures that accommodate for correlation between linear combinations (such as MaxT, MinP, Tukey etc) be used to control the FWE for any set of non-orthogonal contrasts? That is to say, could I construct a contrast-matrix involving any set of non-orthogonal vectors with overlapping information and still be sure that FWE is controlled and that the interpretation is valid? (Given that the contrast had a valid interpretation to begin with...)

Joka · ‎05-12-2014

Thank you for your answer! I will try to find some simulation studies to determine power in these cases or conduct some myself!

Joka · ‎05-06-2014

Steve, I purchased the book and I am well on my way of working through it, it was, just as you said, a really good source when working with designed experiments. Thank you! I have a one more question that I was hoping you could answer. I am currently working through the examples in the chapter we previously discussed and I was wondering if it would be appropriate to use the maxT/minP method when analysing experiments in a randomized complete block design. I understand that the subset pivotality condtion is depedent on the hypotheses formed to get a strong control of the FWE. However, in the case where we might suspect heteroscedasticity among group levels of treatment could the single-step maxT or minP be the an appropriate solution? Are there any pitfalls that you are aware of?

Joka · ‎04-03-2014

I am still a bit uncertain as to what kind of test is performed under heteroscedasticity and how close to the nominal levels of the presented p-values we could get. Is the method well explained in Multiple Comparisons and Multiple Tests Using SAS, 2nd ed.? I might just pick up a copy to understand this method better!

Joka · ‎04-03-2014

I look this up! Is the MaxT adjustment implemented in SAS?

Joka · ‎04-02-2014

I have found procedures such as Tamhanes T2, Dunnets T3 and the Games & Howell procedure that deal with unequal variances in the one-way model. However, I have a Randomized complete block design, which is basically a two-way model. And variances differ quite a lot in the treatment group. In SAS for mixed models 2nd ed. (p. 369) Ramon C. Litell et. al., uses a unequal variance model, proc mixed data=TV ic; class age sex; model time=sex|age/DDFM=KR OUTP=R; repeated / group=age; lsmeans age sex / diff adjust=Tukey; run; However, I am not sure this is correct since the multiple comparison test (Tukey), uses a pooled estimate for the variance thus affecting p-values when the variances are unequal. Is this the correct way of performing multiple comparison under unequal variance? If yes, how does the Tukey adjustment handle the unequal variances?

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Multiple comparison tests that account for correlation between linear ...

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Multiple comparison tests that account for correlation between linear ...

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Re: Multiple comparison in the unequal variance case using PROC MIXED

Multiple comparison in the unequal variance case using PROC MIXED

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