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Joshua ☆ India, 2011-10-11 09:26 (5369 d 16:31 ago) Posting: # 7464 Views: 5,690 |
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Dear All, If a drug is highly variable and has a very long half life, can we go for a parallel study design with three groups i.e. R T and R and do the reference scaled averaging. If yes, please guide me on how to go about it.  Thank You, Joshua. — Joshua. |
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d_labes ★★★ Berlin, Germany, 2011-10-11 11:21 (5369 d 14:37 ago) @ Joshua Posting: # 7466 Views: 4,915 |
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Dear Joshua! The answer is a strict and clear No. Reference scaled average bioequivalence (regardless of using the FDA reference scaled criterion described in the progesteron guidance or the widening of the BE acceptance limits according to the EMA) is defined based on the within-subject variability of the Reference. Moreover the classification of a highly variable drug or drug product (formulation) is also based on the within-subject variability with a CV >30%. Within a parallel group study you are not able to extract the within-subject variability. You only can get the total variability (sum of between and within). Regardless of using two groups ( how, why) with reference administered as you suggest. Therefore you don't have any foundation to apply the reference scaling approach.BTW: The term "Reference Scaled Averaging" is quite sloppy in describing what you intend. There is no such "averaging" in the underlying methods. BTW2: How do you distinguish the "two groups" treated with Reference? Do you have any feature different between them? If yes you have a 3-group parallel design with two distinct References and one Test. — Regards, Detlew |
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Joshua ☆ India, 2011-10-14 10:26 (5366 d 15:31 ago) @ d_labes Posting: # 7492 Views: 4,661 |
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Thanks a ton for your reply Labes. ❝ BTW2: How do you distinguish the "two groups" treated with Reference? Do you have any feature different between them? If yes you have a 3-group parallel design with two distinct References and one Test. Here we intented for the same reference given to two groups and one test. Thank you Edit: Full quote removed. Please delete anything from the text of the original poster which is not necessary in understanding your answer; see also this post! [Ohlbe] — Joshua. |
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Helmut ★★★   Vienna, Austria, 2011-10-14 16:40 (5366 d 09:18 ago) @ Joshua Posting: # 7495 Views: 4,824 |
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Dear Joshua! ❝ Here we intented for the same reference given to two groups and one test. What do you mean by ‘two groups’ here? Do you mean that the final sample size of the ‘reference group’ is twice that of the ‘test group’ (NR1=NR2=NT; NR=2×NT)? Your design will be extremely imbalanced. In the calculation of the confidence interval different sample sizes will be accounted for (see how the pooled variance is calculated here). If you increase the sample size of the reference, its mean will be more precise – but why would you not want to estimate the mean of the test with the same precision? If you want to be really creative you can discover the field of ‘matched pairs’. This concept is sometimes (!) used in clinical trials to adjust for confounding covariates. It’s a reasonable assumption that plasma concentration correlates with body weight (or surface or …). You can pair subjects according to this covariate and run a ‘matched pairs t-test’.* I have never seen any BE study exploring this method; I would expect regulatory acceptance to be close to nil.
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