narra1813 ☆ India, 20190810 09:21 (524 d 19:10 ago) Posting: # 20491 Views: 2,402 

Dear All, By using the only power and sample size how we can calculate the N for NTI molecules. what about the other parameters. Calculating the probability of passing (power) and the sample size (to meet a desired power target) are just two sides of the same coin. One determines the power at a fixed value of n (number of subjects) and the other determines n for a fixed value of power. FDA published a paper that gives the results of the correct simulations; Jiang W, et al, A Bioequivalence Approach for Generic Narrow Therapeutic Index Drugs: Evaluation of the ReferenceScaled Approach and Variability Comparison Criterion, The AAPS Journal, 17(4), 2015. Please provide your valuable suggestion on the same, for my understanding the same. Edit: I deleted another post with identical text. Please follow the Forum’s Policy. I activated the PMfunction and edited your profile. [Helmut] — Regards Narra Narendra Babu 
Helmut ★★★ Vienna, Austria, 20190810 11:23 (524 d 17:07 ago) @ narra1813 Posting: # 20492 Views: 2,013 

Hi Narra, » By using the only power and sample size how we can calculate the N for NTI molecules. what about the other parameters. As with any other referencescaling method we have to use simulations. The conditions for BE of NTIDs are summarized in this post and the statistical method is given in the FDA’s warfaringuidance. Sample size estimations are implemented in function sampleN.NTIDFDA() of the package PowerTOST since 2013. Example: CV_{wT} = CV_{wR} = 10%, expected T/Rratio 97.5%, desired (target) power 80%.
Let’s explore what happens if T has a higher variability (15%) than R (10%). Abbreviated output.
Note that the FDA wants a 4period full replicate. If you are concerned about blood loss and/or dropouts you may consider a 3period full replicate (TRTRTR). Since that deviates from the guidance, I recommend to initiate a controlled correspondence with the OGD first. Like the first example but for 90% power:
We can also explore deviations from our assumptions (only equal CVs implemented). For the first example, minimum acceptable power 70% (default of the function):
Note the interesting behavior of power with various CVs. If the CV gets smaller, limits get tighter and power drops. On the other hand, if the CV increases, we have wider limits and gain power. If the CV_{wR} >21.42% the additional criterion “must pass 80–125%” becomes increasingly important and power drops. We can also explore which of the three criteria are most important. Again the first example and the estimated sample size 18:
The second example where CV_{wT} > CV_{wR}:
» FDA published a paper* … » Please provide your valuable suggestion on the same, for my understanding the same. Hope the above helps.
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