Relationship between calculated 90% CI and sign. treatment effect in BE [General Sta­tis­tics]

posted by GM – India, 2017-03-29 14:11 (2584 d 00:00 ago) – Posting: # 17202
Views: 8,736

Hi David,

❝ The problem might be on the estimate statement. If the 90% CI is significantly different from 1, then the p-value for "formulation" should also be significant at the 10% significance level. Maybe you're using a different denominator than expected...


❝ It would be easier if you post the full glm code.


Here is the code used for my analysis.

Proc GLM data=logdata;
   class Sequence Subject Period Form Cohort;
   model Log(Param) = Sequence Period Form Cohort Form*Cohort Subject(Sequence*Cohort)/ SS3;
   output out=outlier rstudent=student;
   test h=Sequence e=Subject(Sequence*Cohort) / htype=3 etype=3;
   lsmeans Form / pdiff CL alpha=0.10;
   estimate 'A VS B' Form 1 -1;
run;


I got the values for CI is 77.27-98.78 (doesn't contain 100%) and p-value of treatment effect is 0.0709 (not significant at the 5% level).

My observation is that when removing the Form*Cohort term from the model, treatment effect is significant @5% level of significance.

My question is that the terms which are used in the model are sufficient or not...? and if it is correct, why p-value of treatment effect is not significant...?:confused:

Thanks,
GM.

Best Regards,
GM

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