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06-03-2015 10:26 AM

What is the best way to obtain adjusted means from a Proc GLM model (file attached) such as:

proc glm data=invivo;

class block treatment;

model invivo=block treatment;

weight treatment;

lsmeans treatment/ ADJUST=TUKEY;

Contrast 'Compare stx1-0hr with stx2-0hr' treatment 1 0 0 0 -1 0 0 0 0;

Contrast 'Compare stx1-1hr with stx2-1hr' treatment 0 1 0 0 0 -1 0 0 0;

Contrast 'Compare stx1-2hr with stx2-2hr' treatment 0 0 1 0 0 0 -1 0 0;

Contrast 'Compare stx1-3hr with stx2-3hr' treatment 0 0 0 1 0 0 0 -1 0;

run;

The block effect is highly significant but I am having trouble identifying a method to report the adjusted means which accounts for the significant block effect. Does this require the ESTIMATE function?

Any suggestions?

Thank you.

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Solution

06-05-2015
02:10 PM

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06-05-2015 02:10 PM

The best way is to analyze the data using PROC MIXED or PROC GLIMMIX, and consider block as a random effect. In this case, standard errors of the treatment means will correctly incorporate the variance due to block. I also notice in your contrasts that there is a time factor. If the time reflects repeated measurements on the same experimental unit, then the analysis should also incorporate that. Until that is clear, consider the following:

proc glimmix data=invivo;

class block treatment;

model invivo=treatment;

random block;

lsmeans treatment;

LSMESTIMATE treatment

'Compare stx1-0hr with stx2-0hr' 1 0 0 0 -1 0 0 0 0,

'Compare stx1-1hr with stx2-1hr' 0 1 0 0 0 -1 0 0 0,

'Compare stx1-2hr with stx2-2hr' 0 0 1 0 0 0 -1 0 0,

'Compare stx1-3hr with stx2-3hr' 0 0 0 1 0 0 0 -1 0/adjust=simulate(seed=1);

/*Tukey adjustment is not available for the LSMESTIMATE as it is not defined */

run;

Steve Denham

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Solution

06-05-2015
02:10 PM

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06-05-2015 02:10 PM

The best way is to analyze the data using PROC MIXED or PROC GLIMMIX, and consider block as a random effect. In this case, standard errors of the treatment means will correctly incorporate the variance due to block. I also notice in your contrasts that there is a time factor. If the time reflects repeated measurements on the same experimental unit, then the analysis should also incorporate that. Until that is clear, consider the following:

proc glimmix data=invivo;

class block treatment;

model invivo=treatment;

random block;

lsmeans treatment;

LSMESTIMATE treatment

'Compare stx1-0hr with stx2-0hr' 1 0 0 0 -1 0 0 0 0,

'Compare stx1-1hr with stx2-1hr' 0 1 0 0 0 -1 0 0 0,

'Compare stx1-2hr with stx2-2hr' 0 0 1 0 0 0 -1 0 0,

'Compare stx1-3hr with stx2-3hr' 0 0 0 1 0 0 0 -1 0/adjust=simulate(seed=1);

/*Tukey adjustment is not available for the LSMESTIMATE as it is not defined */

run;

Steve Denham

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06-08-2015 10:13 AM

Many thanks Steve. I appreciate your time and expertise.

Sincerely,

John