Mystery [Design Issues]

posted by Helmut Homepage – Vienna, Austria, 2016-03-29 19:28 (3280 d 04:53 ago) – Posting: # 16152
Views: 23,776

Hi Astea,

❝ Recently the customers received a reply from regulatories to calculate aposteriory power and in the case of insufficient power present a plan for further clinical development of the drug.


I’ll offer the agency a free training if travel and accommodation are covered. ;-)
What is sufficient for them? Anything ≥80%? Smells of the end of Appendix 3 of Russia’s GLs of 2004 and 2008:

        We could resolve and reverse task – knowing study population size n, coefficient of variation CV, value of difference Ω and significant level α we could estimate statistical power of bio­equivalence evaluation. To make this possible you should use table of Student distribution and estimate probability of second level error β based on Student t-value, calculated by following equation (in assumption of mean values equality):
[image]
        In case of disruption the assumption for mean values equality the equation could be modified like this:
[image]
        Statistical test power must be not less than 80%.

(my emphasis)

BTW, these GLs were always a mystery to me. What is meant by this part of in Section 4.2. Number of participants?

        During statistical comparison if the power appears to be less than 80%, in those cases when study drugs are not bioequivalent, to draw a reasonable conclusion about nonbioequivalence, study population must be enlarged.


Sure, if one can expect to demonstrate BE in a reasonably larger sample size one will repeat the study. But if not (say in the study the CV was as expected but the PE terrible)?

library(PowerTOST)
n1 <- sampleN.TOST(CV=0.25, theta0=0.95, targetpower=0.8,
                   print=FALSE)[["Sample size"]]
round(100*CI.BE(CV=0.25, pe=0.85, n=n1), 2)
round(100*power.TOST(CV=0.25, theta0=0.85, n=n1), 2)
n2 <- sampleN.TOST(CV=0.25, theta0=0.85, targetpower=0.8,
                   print=FALSE)[["Sample size"]]
round(100*CI.BE(CV=0.25, pe=0.85, n=n2), 2)
round(100*power.TOST(CV=0.25, theta0=0.85, n=n2), 2)

We plan the study for 80% power (CV 25%, T/R 0.95) in 28 subjects. The PE turns out to be awful (85%). Study fails (CI 75.98–95.10%). Post hoc power 22.74%. Now what? That’s not a “reasonable conclusion about nonbioequivalence”? A failed study can never ever show high post-hoc power! Enlarge the sample size and force bioequivalence? Repeat the study in 330 subjects? Study passes (CI 81.66–88.48%), post-hoc power 80.11%.

❝ I estimated power with the help of power.TOST, based on obtained GMR and CV. The result is: power is less than 80% for AUC and more than 80% for Cmax.

❝ Funny thing, but the drugs were bioequivalent!


Yes, why not? See the rather extreme examples given by zizou above.

❝ What to do in such a situation? :confused:


Don’t know. My diplomatic skills are practically nonexistent. Maybe ElMaestro’s suggestions are a way out.
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