RE: Using MCP-MOD in dose finding for Phase 3
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
response either by model selection or model averaging among the significant
candidate models.
I think you could use the principles of MCPmod even if you use a longitudinal
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 response 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 given
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, combined
with a assumed true dose response curve.
An interesting thought that I actually may explore! (I think the output would
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 the
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 improves
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 into one.
Kind regards
Magnus Åstrand
Senior Clinical Pharmacometrician, Ph.D.
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AstraZeneca
Innovative Medicines | Quantitative Clinical Pharmacology
SE-431 83 Mölndal, Sweden
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[email protected]
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From: [email protected] [mailto:[email protected]] On
Behalf Of Mueller-Plock, Nele
Sent: den 20 mars 2015 13:02
To: [email protected]
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 the
MCP-MOD approach for dose finding based on Phase 2B results and would like to
hear your opinion on this approach. It would be good to get feedback from 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 what I
understood, I can see the following issues:
MCP-MOD
· Only uses trial endpoints, i.e. it ignores the time course of the
treatment effect. I have a problem with this because there might be noise in
the endpoint (e.g. if the effect has reached a plateau), which might
potentially lead to the selection of the wrong model structure. Including the
time-course like in PKPD modeling approaches would detect that the deviation is
just noise, and thus probably be able to identify the right model structure
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 given
the data we have. What happens if the true model is not part of the tested
ones? What if we have new physiological insights that tell us about the model
structure after we have seen the data? Do we then ignore what we know 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 prespecification
and only have a few dose strength, wouldn't the model with more parameters 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
different shapes in the candidate set to avoid potential bias in dose
selection, but they argue that post-hoc model fitting leads to data-dredging
and over-fitting, does not account for model uncertainty and gives
overly-optimistic results. I am wondering however what the difference in the
approach is if anyway ALL potential models are considered (which can lead to
overfitting 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 receiving
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
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8152 Glattpark-Opfikon (Zürich)
Switzerland
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