GM Junior India, 20170327 19:40 Posting: # 17188 Views: 1,416 

Dear All, I have a doubt that, is there any relationship between calculated 90% CI and significant treatment effect in BE studies. If the 90%CI is one side of hundred, i.e. either (85.0098.00) or (102.00119.00) then there should be significant treatment effect. Is this statement correct...? And why we are generally checking the treatment effect at 5% level of significance...? Please clarify. Many thanks in advance. Regards, GM. 
Helmut Hero Vienna, Austria, 20170327 23:47 @ GM Posting: # 17189 Views: 1,296 

Hi GM, » If the 90%CI is one side of hundred, i.e. either (85.0098.00) or (102.00119.00) then there should be significant treatment effect. Is this statement correct...? Absolutely. » And why we are generally checking the treatment effect at 5% level of significance...? The inclusion of a 100(1–2α) confidence interval within the common acceptance range [L, U] of 80–125% is operationally equivalent to Two OneSided tTests (TOST). One ttest is for ≤80% and the other one for ≥125%, both at a level of α 0.05. Null and alternative hypotheses: Inclusion of the 100(1–2α) CI: In BE the Null is inequivalence. Extreme example:
Although the treatmenteffect (and its pvalue) is part of the standard output of most software packages, it is not relevant for the BEdecision. Statistical significant ≠ clinically relevant… I don’t know why you are checking it – I don’t. — All the best, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. ☼ Science Quotes 
GM Junior India, 20170328 06:50 @ Helmut Posting: # 17190 Views: 1,257 

» » If the 90%CI is one side of hundred, i.e. either (85.0098.00) or (102.00119.00) then there should be significant treatment effect. Is this statement correct...? » » Absolutely. Hi Helmut, Thank you for the reply. I was also found that most of the cases above statement is correct only. But I have a situation where treatment effect is not significant even the calculated 90%CI falls one side of hundred. Is it possible..? Actually I was used Proc GLM in SAS for this 2×2 crossover study with the model statement mentioned below: model logCmax = Sequence Period Form Cohort Form*Cohort Subject(Sequence*Cohort)/ SS3; Cohort was mentioned as a fixed effect because the study conducts in two groups. The fixed effects mentioned in above the model will effect the treatment effect...? Please explain. Regards, GM. 
DavidManteigas Regular Portugal, 20170328 11:48 @ GM Posting: # 17191 Views: 1,229 

Hi GM, The problem might be on the estimate statement. If the 90% CI is significantly different from 1, then the pvalue 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. 
ElMaestro Hero Denmark, 20170328 13:12 @ DavidManteigas Posting: # 17192 Views: 1,226 

Hi DavidManteigas, » If the 90% CI is significantly different from 1, then the pvalue for "formulation" should also be significant at the 10% significance level. Maybe you're using a different denominator than expected... (...) the pvalue for "formulation" should also be significant at the 5% significance level assuming we work with alpha=5% and therefore 90% CI's. Alpha is alpha, unless we talk twostage approaches and the like. — I could be wrong, but… Best regards, ElMaestro  since June 2017 having an affair with the bootstrap. 
d_labes Hero Berlin, Germany, 20170328 15:12 @ ElMaestro Posting: # 17193 Views: 1,208 

My Dear! » (...) the pvalue for "formulation" should also be significant at the 5% significance level assuming we work with alpha=5% and therefore 90% CI's. » Alpha is alpha ... I think here you err. See this thread (evaluation without group effect): pvalue of treatment effect = 0.076998 (not significant at the 5% level) 90% CI = 90.76 ... 99.62% (doesn't contain 100%) — Regards, Detlew 
ElMaestro Hero Denmark, 20170328 21:10 @ d_labes Posting: # 17195 Views: 1,160 

Hi d_labes, » pvalue of treatment effect = 0.076998 (not significant at the 5% level) » 90% CI = 90.76 ... 99.62% (doesn't contain 100%) Thank you. This somehow does rock my fundamental understanding. I need to sit down with pen and paper and work it out. If anova p<0.05 for treatment then the 90% CI does not include 1.00. But vice versa isn't the case?!??? This is not at all intuitive to me since the Null is equality at alpha 5%. I must look a bit into this, I believe. — I could be wrong, but… Best regards, ElMaestro  since June 2017 having an affair with the bootstrap. 
d_labes Hero Berlin, Germany, 20170329 08:20 @ ElMaestro Posting: # 17197 Views: 1,145 

Dear Öberster Größter Meister, "duality confidence interval hypothesis testing" may help in understanding. — Regards, Detlew 
DavidManteigas Regular Portugal, 20170329 11:16 @ ElMaestro Posting: # 17200 Views: 1,114 

Hi d_labes and ElMaestro, I'm also struggling with the question now. A 90% CI compares with a hypothesis test at 10%. The 90% CI is equivalent to a statistical assessment of equivalente at the 5% level due the TOST approach, since you're not assessing significance for the null hypothesis of difference in means. Nevertheless, when you apply the model the 90% CI interval is an interval for difference in means regardless of the interpretation of the results in the bioequivalence context. As the statistical conclusion of "difference in means" is obtained at the 10% level and not 5% level, and the term for formulation is assessing whether there is a "difference in means" and not equivalence, the p value for formulation will be significant at the 10% significance level if the 90% confidence interval does not contains 1. So it is completly plausible for me to have a 90% CI without 1 and a nonsignificant p value for formulation at the 5% significance level. Am I understanding the issue wrongly? Regards, David 
ElMaestro Hero Denmark, 20170329 11:20 @ DavidManteigas Posting: # 17201 Views: 1,120 

Hi DavidManteigas and d_labes, » I'm also struggling with the question now. A 90% CI compares with a hypothesis test at 10%. The 90% CI is equivalent to a statistical assessment of equivalente at the 5% level due the TOST approach, since you're not assessing significance for the null hypothesis of difference in means. I beg to differ; the 90% CI approach applies a 5% alpha. A product which in not truly BE (GMR is 0.8 or below; or 1.25 or higher, can't be both), will have at most 5% chance of passing BE; te CI is made from 1.02alpha but that does not mean 10% chance of approving a nonBE product. It is the same alpha 5% that is used in the ANOVA where the null hypo is sameness. If I am wrong here then it is my very basic understanding of statistics that needs thorough remodeling. — I could be wrong, but… Best regards, ElMaestro  since June 2017 having an affair with the bootstrap. 
DavidManteigas Regular Portugal, 20170329 12:28 @ ElMaestro Posting: # 17203 Views: 1,096 

Hi ElMaestro, What I've said is that the pvalue for formulation as nothing to do with the statistical conclusion of bioequivalence. For the statistical evaluation of "difference in means", to be compared with the same statistical conclusion of "difference in means" with the 90% confidence interval, the p value must be assessed against the 10% significance level. So, if your 90% Confidence Interval does not contain 1, then the pvalue for formulation is also significant at the 10% significance level and may or not be also significant at the 5% significance level. The model is build the same way whether you are assessing difference in means or bioequivalence. The hypothesis are, however, different. For the hypothesis of "bioequivalence" the alpha level is 5%, and for the hypothesis of "difference in means" the alpha level is 10%. And the pvalue only assess the hypothesis of difference in means. I'm sorry if I'm not explaining myself right, I'll let someone smarter try to do a better job 
d_labes Hero Berlin, Germany, 20170329 14:24 @ DavidManteigas Posting: # 17205 Views: 1,084 

Dear David, Dear ElMaestro! » The model is build the same way whether you are assessing difference in means or bioequivalence. The hypothesis are, however, different. Totally correct. The equivalence test is TOST (twoonesided tests) each with alpha=0.05 which is operationally equivalent to 12*alpha CI. Difference test has a twosided alternative H_{A}, and thus is dual to an 1alpha CI, i.e. 95%CI if you set alpha to 0.05. The 95% CI of the above cited example is 95% CI = 89.86 ... 100.61% (contains 100%) so no significant treatment effect, same conclusion as via the ANOVA pvalue.— Regards, Detlew 
GM Junior India, 20170329 20:06 @ DavidManteigas Posting: # 17209 Views: 1,038 

Dear David, » For the statistical evaluation of "difference in means", to be compared with the same statistical conclusion of "difference in means" with the 90% confidence interval, the p value must be assessed against the 10% significance level. So, if your 90% Confidence Interval does not contain 1, then the pvalue for formulation is also significant at the 10% significance level and may or not be also significant at the 5% significance level. As Helmut also told in the above discussion, generally we are assessing the treatment effect @5% significance level only. But based on your statement, it is 10% level of significance. Now I am in confusion ... whether 5%level or 10% level of significance we need to consider for BE...? Please clarify. Thanks. Regards, GM 
nobody Senior 20170330 08:24 @ GM Posting: # 17210 Views: 994 

Helmut wrote: "The inclusion of a 100(1–2α) confidence interval within the common acceptance range [L, U] of 80–125% is operationally equivalent to Two OneSided tTests (TOST). One ttest is for ≤80% and the other one for ≥125%, both at a level of α 0.05." Two tests (<80%, >125%), each with a 5% chance to fail, 5+5=10, gives an overall 10% chance to fail... Therefore 90% CI is equivalent to the TOST with 5% each. — Kindest regards, nobody 
GM Junior India, 20170329 12:11 @ DavidManteigas Posting: # 17202 Views: 1,108 

Hi David, » The problem might be on the estimate statement. If the 90% CI is significantly different from 1, then the pvalue 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; I got the values for CI is 77.2798.78 (doesn't contain 100%) and pvalue 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 pvalue of treatment effect is not significant...? Thanks, GM. 
DavidManteigas Regular Portugal, 20170329 12:30 @ GM Posting: # 17204 Views: 1,094 

Hi Gm, Your code seems ok to me. In my opinion, your result is completly normal since the pvalue is significant at the 10% significance level, which compares with the 90% CI with obtained. ElMaestro has a different opinion though, so you should follow our discussion Regards, David 