Williams design 3-way [Design Issues]

posted by vezz – Erba (CO), Italy, 2021-06-11 18:56 (1035 d 19:05 ago) – Posting: # 22411
Views: 8,492

❝ I was a little bit hesitant to answer, as I am and likely will remain a noob in the statistical background. Maybe I also misunderstand your question, which is quite possible.

❝ But there are a lot of people around here to correct the answer, if necessary, and its SOP-Friday...

❝ I think I get your point, but I also think that the period effect is implemented above.

❝ The numbers already include both as mean=µ+π = treatment + period effect.

Hi Relaxation and Helmut,

the period effect was taken into account by Helmut when generating the data, but not when analysing them.

I am not able to explicitly describe each single step behind the estimation of the regression model (this would take some time!), but I will share with you the SAS code replicating the example.

Some notes:
- Treatments are coded as 1-2-3 instead of A-B-C.
- Without loss of generality, I am assuming 6 subjects per sequence and with the PARMS statement in the MIXED procedure I keep the residual variance fixed at 10.
- In the first MIXED procedure a model not including the period effect is estimated, while in the second one the model includes the period effect.

data a01 (drop=i t1-t3);
   input t1 t2 t3;
   do i=1 to 6;   
      period=1; tmt=t1; output;
      period=2; tmt=t2; output;
      period=3; tmt=t3; output;
   1 2 3
   2 1 3
   3 1 2
   3 2 1

data a02;
   set a01;

proc mixed data=a02;
   class seq subj tmt;
   model y = seq subj(seq) tmt;
   parms 10 / hold=1;
   lsmeans tmt / diff;

proc mixed data=a02;
   class seq subj period tmt;
   model y = seq subj(seq) period tmt;
   parms 10 / hold=1;
   lsmeans tmt / diff;

Estimated treatment differences (SE) by the first model (no period effect):
- A vs. B: 3.89E-16 (0.9129)
- A vs. C: -0.3750 (0.9129)
- B vs. C: -0.3750 (0.9129)

Estimated treatment differences (SE) by the second model (period effect included):
- A vs. B: 3.7E-16 (0.9129)
- A vs. C: -753E-17 (1.0206)
- B vs. C: -79E-16 (1.0206)

We may notice that in the second model:
- The estimate of the treatment effect is always practically 0, therefore unbiased.
- SEs are not identical as the design is not balanced for period.

Kind regards,


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