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Shuanghe ★★ Spain, 2012-01-26 10:44 (4857 d 04:13 ago) Posting: # 8005 Views: 4,786 |
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Dear all, There're many talks on FDA's scaled BE approach here but I couldn't find the topic on subject to analyse/include in BE protocol. Here it goes: I've been reviewing a BE report, TRTR/RTRT full replicate. In the protocol it says that subjects who complete at least 2 periods (1 test and 1 reference product) will be assayed. it make sense for 2x2 standard BE that product should be crossed at least once. In addition, " (sth. about average approach...) Only subjects who have evaluable data from at least one evaluable period of Test product and both evaluable periods for Reference product will be included in the reference-scaled bioequivalence analyses...." My question is: is this phrase correct? According to the FDA's draft guidance on progesterone, in order to apply SABE, Swr should be >= 0.294 and this value is calculated solely from Reference product (Dij). So for those subjects who has only 2 periods, both reference, shouldn't they also be included in the analysis for the sake of Dij, hence the Swr, calculation? Theoretically, with or without those subject, Swr might be >= or < 0.294 so it might affect the applicability of SABE. Of course, in the study report I've been reviewing there's no such subject but it would be nice to know your opinion for the studies in the future. More general question is: how to treat the topic of "Subject to Analyse" in replicate (partial or full) BE study? Thanks. -------- Edit: Just checked EMA's BE guideline and PK working party's QA. BE guideline requires crossing at least once so subject included should at least have 1 test and 1 reference and QA specified for replicate study Swr should calculated only with reference data (remove test data). So the above question applies to European BE study as well: in the protocl, should we specify to include subject who has only 2 periods of reference? Shuanghe, Edit: Category changed. [Helmut] — All the best, Shuanghe |
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d_labes ★★★ Berlin, Germany, 2012-02-23 11:46 (4829 d 03:11 ago) @ Shuanghe Posting: # 8165 Views: 3,597 |
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Dear Shuanghe! ❝ More general question is: how to treat the topic of "Subject to Analyse" in replicate (partial or full) BE study? Good question! Next question  .Seems we here don't have an answer or have overlooked this important post. Let me throw in my two cents: I'm not aware of any regulatory recommendation about that topic. I personally would prefer to analyse the data as are, i.e. include all available reliable data, missing periods or not. This could be done if we would evaluate with an mixed model (Proc MIXED in SAS or equivalent lme in R) considering the subjects as random effect what they IMHO really are.Unfortunately the European great oracle EMA doesn't allow us to do so. Remember the "all effects fixed" recommendation. Thus subjects having not at least one PK metric under Test and Reference will not contribute to the estimation of the treatment effect (ABE criterion). Regarding the EMA recommended scaled ABE method subjects with one missing under Reference will not contribute to the estimation of the intra-subject variability for Reference. Interesting enough the dataset I of the EMA Q&A contains such subjects with one missing for Test or Reference and you can see that they are not skipped from the analysis. To summarize: Seems the EMA in applying its recommended method doesn't exclude subjects with missing data. Then the two parts of the scaled ABE criterion (point estimator of µT - µR with its confidence interval and on the other hand the estimator of the intra-subject variability for reference rely on different numbers of subjects. This also applies to the FDA scaled ABE method (SAS code in Progesterone guidance, current Feb 2011 version) if one doesn't exclude subjects due to missingness beforehand. I personally are not comfortable with this as it is well known that missings in the method of moments (and both FDA and EMA method are some sort of) may lead to biased estimators. And no one is aware of the magnitude of influence of this effect on the BE decision. This is not the case for complete data or evaluation via mixed models. Thus if mixed models are not "allowed" it would be wise to exclude subjects with any missing. But this will nearly certainly not accepted by the regulatory authorities I think. Cough, much ado about nothing  .Any other opinions out there? — Regards, Detlew |
Ing. Helmut Schütz