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

From: Åstrand, Magnus <Magnus.Astrand_at_astrazeneca.com>
Date: Fri, 20 Mar 2015 17:47:59 +0000

Dear Nele, here are some thoughts:

The idea with the MCPmod is twofold,
a) provide a procedure for testing for a treatment effect and in that test =
incorporate all doses studies and still maintain control of type I error.
b) If significance in a) continue with framework for estimating the dose re=
sponse either by model selection or model averaging among the significant c=
andidate models.

I think you could use the principles of MCPmod even if you use a longitudin=
al model with a time course of your treatment effect.
You could for example use the same time profile for the treatment effect in=
 all doses, but estimate different magnitude for each dose. (indirect respo=
nse model with effect on kin, one level for each dose)
The estimated magnitudes would then replace the mean effect in each dose in=
 the standard MCPmod application.

The theory of MCPmod builds on the existence of a optimal contrast for a gi=
ven true effect profile across your set of doses.
Potentially there is a way to derive optimal tests but instead base that on=
 a assumed distribution of the exposure across all your doses included, com=
bined with a assumed true dose response curve.
An interesting thought that I actually may explore! (I think the output wou=
ld be a weight function w(exposure) so that you would get a test based on w=
(exposure)*observed_effect, sum across all your data.

There is no limit on how many candidate models you can use, so I don't see =
that as a problem.
Planning of your analysis across a wide range of potential DR functions to =
make sure you have good power whatever the true DR is recommended.
(And actually by selecting a smart set of candidate models can improve on t=
he power)
You can include several emax, but with different set of parameters, combine=
 that with other types of functions, sigmod emax.

On your last bullet, a good way around is to use model averaging instead of=
 model selection. If your model with more parameters only marginally improv=
es the fit, the weight for that model will not be so high.
My experience is that model averaging generally performs better than model =
selection. A big advantage is also if you end up with 2 equally good models=
, instead of presenting 2 results to your project, you combine them both in=
to one.

Kind regards

Magnus Åstrand
Senior Clinical Pharmacometrician, Ph.D.

Innovative Medicines | Quantitative Clinical Pharmacology
SE-431 83 Mölndal, Sweden
T: +46 (0)31 776 23 41
Mob: +46 (0)708 467 667

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From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On=
 Behalf Of Mueller-Plock, Nele
Sent: den 20 mars 2015 13:02
To: nmusers_at_globomaxnm.com
Subject: [NMusers] Using MCP-MOD in dose finding for Phase 3

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:

· 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 :-)


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

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

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

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mailto: nele.mueller-plock_at_takeda.com<mailto:nele.kaessner_at_nycomed.com>


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Received on Fri Mar 20 2015 - 13:47:59 EDT

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