Confounded effects; common variance [General Statistics]
❝ We are preparing a 4-period single-dose BE study under both fed and fasting conditions (as described in the new draft BE/BA EMEA guidance: Test fast/Ref fast/Test fed/Ref fed).
You know that a draft is a draft is a draft.

At the EUFEPS-workshop Kamal Midha and myself strongly argued against the suggested design. Below my comments I have sent to EMEA in January 2009.
It should be noted that in a four-period single dose crossover design study (both products fed and fasted) treatment effects and food effects are massively confounded and no unbiased estimates can be obtained.
In a BE study the main effect of interest is ‘treatment’ (≥2 different formulations, but either in fasting or in fed state). In a food effect study it is ‘food’ (using the same treatment). One of the main assumptions in the usual (nonreplicate) cross-over model is an Independent Identically Distribution (IDD) of effects. This assumption simply may not hold. If e.g., the variability of the reference is higher than the one of the test, one will obtain a high common variance and the test will be penalized for the reference performing badly. For most MR formulations one yet would expect different variabilities in fasting and fed state. Even for IR formulations food will change liver blood flow → hepatic clearance → not only the absorption, but also the elimination may be altered (note: constant clearance is the main assumption in BE). Since the suggested design study is of a nonreplicate design with 2 effects (2 levels: fasting|fed, 2 levels: T|R) the assumption of a common variance is downright absurd.
An alternative to two different studies (Tfasted|Rfasted and Tfed|Rfed), where an inter-study comparison as parallel groups (Tfed vs. Tfasted and Rfed vs. Rfasted) is lacking power, a 2-sequence, 4-period design of following type would avoid confounding issues:
Tfasted Rfasted Tfed Rfed
Rfasted Tfasted Rfed Tfed
In such a design treatments in periods 1 and 2 can be compared in fasted state and in periods 3 and 4 in fed state as a conventional cross-over. Additionally Tfed vs. Tfasted and Rfed vs. Rfasted can be evaluated as a paired design (with high power, but avoiding confounding issues).
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
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Helmut Schütz
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Science Quotes
Complete thread:
- 4-period BE study fed/fasting: statistical issues Amandine 2009-06-04 12:31
- Confounded effects; common varianceHelmut 2009-06-04 12:55
- Neglecting period effects? d_labes 2009-06-10 10:10
- Assumptions... Helmut 2009-06-23 14:38
- What if... Ohlbe 2010-03-10 14:27
- What if... Dr_Dan 2010-03-11 15:50
- What if… Helmut 2010-03-11 16:39
- What if… Dr_Dan 2010-03-12 08:49
- Need-to-know / nice-to-know Helmut 2010-03-12 11:35
- many ANOVA but only one trial boonchai_l 2010-05-24 11:53
- many ANOVA but only one trial GSTATS 2010-06-08 21:52
- many ANOVA but only one trial boonchai_l 2010-05-24 11:53
- Need-to-know / nice-to-know Helmut 2010-03-12 11:35
- What if… Dr_Dan 2010-03-12 08:49
- What if… Helmut 2010-03-11 16:39
- What if... Dr_Dan 2010-03-11 15:50
- Neglecting period effects? d_labes 2009-06-10 10:10
- Confounded effects; common varianceHelmut 2009-06-04 12:55
