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BE-proff ● 2015-11-05 19:31 (3427 d 22:11 ago) Posting: # 15615 Views: 9,670 |
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Hi All, Unless I am mistaken ANOVA assesses influence of different factors (subjects, treatment, period, sequence) on variation of PK-parameters. In my practice only subjects have always had significant impact on variation. What can be reasons for signficant influence of treatment, period and sequence?  Is it bad or not?  |
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Helmut ★★★   Vienna, Austria, 2015-11-05 20:33 (3427 d 21:09 ago) @ BE-proff Posting: # 15617 Views: 8,725 |
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Hi BE-proff, ❝ […] ANOVA assesses influence of different factors (subjects, treatment, period, sequence) on variation of PK-parameters. OK; you are talking about a crossover. ❝ In my practice only subjects have always had significant impact on variation. Anything else would be a big surprise. Subjects are different indeed. ❝ What can be reasons for signficant influence of treatment, period and sequence? Treatment Happens if the the CV is smaller + the T/R is more far away from 100% than assumed in study planning; the confidence interval does not include 100%. Reasons: The formulation and/or the study planning (overpowered?)
Doesn’t matter at all. Since both R and T are affected to the same degree, true differences will mean out. Try it: Take one of your datasets and multiply all results of the second period by 1,000. The PE and CI of T/R will be exactly the same like in the original dataset. Of course the period effect comes out highly significant. Reasons: Generally unknown (subjects more relaxed in the 2nd period, lunar phase, thunderstorm, broken AC, whatsoever). Sequence Better called unequal carryover. It essentially means that subject’s PK response might be different if they receive formulations in the order RT as compared to the order TR. Tricky. If there would be a true sequence effect we cannot get an unbiased estimate of the treatment effect (technically these effects are “confounded”). That’s bad.
A test for carry-over is not considered relevant and no decisions regarding the analysis (e.g. analysis of the first period only) should be made on the basis of such a test. The potential for carry-over can be directly addressed by examination of the pre-treatment plasma concentrations in period 2 (and beyond if applicable). ❝ Is it bad or not? Define bad. — Dif-tor heh smusma 🖖🏼 Довге життя Україна! ![[image]](https://static.bebac.at/pics/Blue_and_yellow_ribbon_UA.png) Helmut Schütz ![[image]](https://static.bebac.at/img/CC by.png) The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
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felipeberlinski ☆ Brazil, 2015-11-05 20:56 (3427 d 20:46 ago) @ Helmut Posting: # 15618 Views: 8,328 |
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Hi Helmut and others In case of performing a study with an endogenous substances such as hormones what would be the approach to justify an ocurrance of a sequence effect for HA? Premisse: You have an adequate washout period, and you have found this effect....  Some stats that I have contact suggested a bootstrap analysis to check if it was a random or a real effect... What else could be done? |
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Helmut ★★★ Vienna, Austria, 2015-11-05 21:27 (3427 d 20:16 ago) @ felipeberlinski Posting: # 15619 Views: 8,384 |
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Hi Felipe, ❝ In case of performing a study with an endogenous substances such as hormones what would be the approach to justify an ocurrance of a sequence effect for HA? Premisse: You have an adequate washout period, and you have found this effect.... Endogenous compounds are of a nasty kind. I can only suggest to keep in mind that there might be a feedback loop. It’s not only laking concentrations in the higher periods which counts. Keep the washout as long a possible. If you followed all that (as you did), maybe the Type I Error hit (you got a significant effect, which is not there). You can try to claim that. Not sure whether regulators(s) will buy it. If you want to be sure, I’m afraid there is no way around a parallel design. In all (!) crossover studies of biosimilars I’m aware of there was a significant sequence effect. Something happened to the body. Given that it is beyond me why biosimilar-guidelines recommend a crossover design as the “gold standard”. ❝ Some stats that I have contact suggested a bootstrap analysis to check if it was a random or a real effect... Sounds like black magic to me. But I’m not a statistician.  — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
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ElMaestro ★★★ Denmark, 2015-11-05 22:28 (3427 d 19:15 ago) @ felipeberlinski Posting: # 15621 Views: 8,266 |
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Hi f.b., ❝ In case of performing a study with an endogenous substances such as hormones what would be the approach to justify an ocurrance of a sequence effect for HA? Premisse: You have an adequate washout period, and you have found this effect.... ❝ What else could be done? If P(Seq) is low then it means your metric objectively differed between the two sequences. Perhaps the randomisation "was not effective"; check your descriptive statistics. If all the fat ones ended up in TR and all the Jane Fonda types were in RT then that would be one obvious reason, but you cannot prove it as a hindsight consideration. It would however, be a likely explanation. Can happen by chance. — Pass or fail! ElMaestro |
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BE-proff ● 2015-11-05 22:27 (3427 d 19:15 ago) @ Helmut Posting: # 15620 Views: 8,232 |
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Dear Helmut, Your explanation is really cool!  But one more question: overpowered study...what do you mean? Power over 100%? Never heard about it  |
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Helmut ★★★ Vienna, Austria, 2015-11-06 05:03 (3427 d 12:40 ago) @ BE-proff Posting: # 15622 Views: 8,304 |
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Hi BE-proff ❝ overpowered study...what do you mean? Generally we plan studies for 80–90% power π (where the producer’s risk of failing to demonstrate BE of a formulation which is BE: Type II Error, β = 1 – π). If you submit a protocol to the IEC which >90%, they might not accept it. Hence, “overpowered”. Sometimes rich sponsor know that the CV will be lower and/or the T/R is closer to 100% than what they present in the sample size estimation. Protocol approved, study done, low risk of failure. They call it “to be on the safe side”. I call it playing dice with the health of subjects. Sometimes the minimum sample size given in guidelines will lead to high power anyway. Example – T/R 0.95, 2×2 crossover, AR 80–125%; CVs which will lead to >90% power:
Imagine: n 134, T/R 0.97, CV 15%. The 90% CI will be 94.12–99.97%. 100% not included, significant treatment effect (p 0.0485), bingo. Is it relevant? Not at all. You can be >99.9999999999999% sure that the true T/R-ratio is not below 80%… ❝ Power over 100%? Negative producer’s risk‽ — Dif-tor heh smusma 🖖🏼 Довге життя Україна! Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes |
Ing. Helmut Schütz