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[NMusers] Using MCP-MOD in dose finding for Phase 3

From: Mueller-Plock, Nele <Nele.Mueller-Plock_at_takeda.com>
Date: Fri, 20 Mar 2015 13:01:34 +0100

Dear all,

I am writing to you as we are currently discussing the implementation of th=
e MCP-MOD approach for dose finding based on Phase 2B results and would lik=
e to hear your opinion on this approach. It would be good to get feedback f=
rom both statisticians and classical modelers.
I have thought about the approach, and have a few problems about seeing the=
 advantage of the approach over complete population-PK/PD modeling. From wh=
at I understood, I can see the following issues:
MCP-MOD

· Only uses trial endpoints, i.e. it ignores the time course of t=
he treatment effect. I have a problem with this because there might be nois=
e in the endpoint (e.g. if the effect has reached a plateau), which might p=
otentially lead to the selection of the wrong model structure. Including th=
e time-course like in PKPD modeling approaches would detect that the deviat=
ion is just noise, and thus probably be able to identify the right model st=
ructure despite this.

· Uses dose-response models instead of exposure-response models

· Pre-specifies the model structure. While I understand that for =
pivotal trials prespecification is crucial, I would assume that Phase 2 is =
performed to allow exploration of the data to come up with the best model g=
iven the data we have. What happens if the true model is not part of the te=
sted ones? What if we have new physiological insights that tell us about th=
e model structure after we have seen the data? Do we then ignore what we kn=
ow and fit all bad models, and if none gives a good description we do model=
 averaging of bad models?

· If we include a model with many parameters in the prespecificat=
ion and only have a few dose strength, wouldn't the model with more paramet=
ers be more likely to give a good fit (e.g. when comparing Emax to logistic=
), with the consequence that a wrong dose might be selected?

Colleagues from statistics recommend to cover all potential models with dif=
ferent shapes in the candidate set to avoid potential bias in dose selectio=
n, but they argue that post-hoc model fitting leads to data-dredging and ov=
er-fitting, does not account for model uncertainty and gives overly-optimis=
tic results. I am wondering however what the difference in the approach is =
if anyway ALL potential models are considered (which can lead to overfittin=
g as well)?
Might a good solution be to combine PKPD modeling with MCP-Mod?

Your opinion will be highly appreciated, and I am looking forward to receiv=
ing comments both in favour and against the approach :-)

Best
Nele
______________________________________________________________

Dr. Nele Mueller-Plock, CAPM
Associate Scientific Director Pharmacometrics
Global Pharmacometrics
Translational Medicine

Takeda Pharmaceuticals International GmbH
Thurgauerstrasse 130
8152 Glattpark-Opfikon (Zürich)
Switzerland

Visitor address:
Alpenstrasse 3
8152 Glattpark-Opfikon (Zürich)
Switzerland

Phone: (+41) 44 / 55 51 404
Mobile: (+41) 79 / 654 33 99

mailto: nele.mueller-plock_at_takeda.com<mailto:nele.kaessner_at_nycomed.com>
http://www.takeda.com<http://www.takeda.com/>


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Received on Fri Mar 20 2015 - 08:01:34 EDT

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