Data imputation [Regulatives / Guidelines]
❝ Recently I've received a letter from the Customer where it was pointed out that according to the planning regulatory documents we have to include missing data in analyses in a very specific way.
You posted in the category Regulatives / Guidelines. What do you mean by “planning regulatory documents”?
❝ For example in several cases the missing data from one volunteer in one period should be replaced be the mean arithmetic of the other's data.
That’s bizarre. Practically all regulatory documents point out that common PK metrics (except tmax) are assumed to follow a lognormal distribution. Hence, we apply a multiplicative model (log-transformation in order to obtain additive effects). Arithmetic mean is wrong.
❝ Of course this method has a right to exist.
Don’t think so.
❝ It was described in European Pharmacopeia 5.0 p.3.2.6 and in several articles, but it seems to me very strange to use this technic in bioequivalence data analyses.
The technical term is “imputation”. It is common in clinical studies if a patient drops out midcourse (i.e., Last-Observation-Carried-Forward – LOCF). Here it can be (!) conservative since in a superiority test the estimated mean difference will me smaller and if one assumes that the treatment performs better than placebo. But it is not that easy. ICH E9 states in Section 5.3:
Unfortunately, no universally applicable methods of handling missing values can be recommended.
I never ever have seen it in BE and for good reasons. If you impute the missing by the geometric (!) mean of the other subjects:
- The between-subject variance likely will be lower than the one of the dataset before imputation.
- The T/R-ratio will be biased to an unknown degree.
- The SE of the difference might be lower as well. Hence such a comparison might be anticonservative.
BTW, the EMA’s GL wants only complete subjects (T and R) in simple crossovers and at least one period with each of T and R in replicate designs. Both the EMA’s and the FDA’s SAS-code for reference-scaling drop incomplete subjects.
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Science Quotes
Complete thread:
- Missing data analyses Astea 2016-04-11 10:33 [Regulatives / Guidelines]
- Data imputationHelmut 2016-04-11 13:13
- Data imputation Astea 2016-04-11 17:53
- Missing data analyses Hutchy_7 2016-04-12 13:38
- Missing sample(s) Helmut 2016-04-12 14:06
- Missing sample(s) Hutchy_7 2016-04-12 15:29
- Missing sample(s) Helmut 2016-04-12 14:06
- Data imputationHelmut 2016-04-11 13:13