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

From: Alan Maloney <al_in_sweden_at_hotmail.com>
Date: Fri, 27 Mar 2015 10:23:33 +0100

Hi Nele/All,

I wanted to (belatedly) add a few comments about MCP-MOD and the =
comments you have received, and Phase 2 design/analysis in general.

In short, I agree with most of the observations made by yourself and =
others, and I would not want to use MCP-MOD (see my comments =
"http://www.ema.europa.eu/docs/en_GB/document_library/Other/2014/02/WC5001=
61028.pdf" and other comments, in particular those of Qing Liu).
 
That said, I would fully agree with Björn, in that it is clearly =
better than pairwise comparisons. Like Mike, I find it incredulous that =
Phase 2 Dose-Exposure-Response (D-E-R) studies are still being designed =
WITHOUT planned D-E-R analyses...the D-E-R is the purpose of the =
study!...hopefully MCP-MOD will continue to generate the types of =
discussions you are having, which is great.


We can consider 5 aspects of Phase 2 design, which overlap with =
different aspects of MCP-MOD

a) The models being considered (the "model space")
b) The design of the study (the "design/data space"...e.g. minimum dose, =
maximum dose, dose spacing, N, observation schedule etc.))
c) The metric of designs performance (e.g. the expected accuracy and =
precision of our potential D-E-R models under alternative study designs)
d) The ability of the design/data to detect a D-E-R relationship
e) The presentation of multiple credible models, and possible model =
averaging.

a) The models being considered (the "model space")

I think that D-E-R models should normally be based around (longitudinal) =
sigmoidal Emax type models. The sigmoidal Emax is special! (1). That is, =
I do not wish to design or analysis my study using a linear (or =
log-linear, umbrella) model, so my candidate set of models clearly =
differ from MCP-MOD. See Neil Thomas's work looking at the =
appropriateness of this model in drug development =
("http://www.ema.europa.eu/docs/en_GB/document_library/Presentation/2015/0=
1/WC500179795.pdf"). Clearly there are multiple options around the =
longitudinal sigmoidal Emax model, including the formulation of the =
longitudinal component, treatment of missing data, =
location/parameterisation of random effects, correlation structure =
between timepoints, use of dose/exposure/concentration (and what PK =
model?), covariate effects etc. This is my "model space". In addition, =
we should put the study data into the framework of external analyses =
(e.g. a Model Based Meta Analysis (MBMA)), where we often have a good =
idea of parameters associated with the expected changes over time, the =
maximal effect for that class of compounds, the effect for a comparator =
arm etc. Thus we can think of both models where we use only the internal =
data AND models where we utilise information from external data. For =
example, if we wished to determine the precision of the D-E-R versus an =
active comparator, we could use the internal arm in the study as the =
reference (say an effect (95% CI) of 1 (0.3, 1.7). However a MBMA may =
put the comparator effect at 1.2 (0.9, 1.5). Clearly both references are =
credible, and the two results are not inconsistent with each other. =
Surely it makes sense to determine the doses to take forward after =
reviewing both sets of results. There are numerous examples =
(correlations between endpoints, turnover parameters, "system" =
components etc.) where we should look to leverage external data and =
understanding to augment our interpretation of the (relatively weak) =
data from a single study.

b) The design of the study ("design/data space"), and c) metrics of =
design performance

The design is critical, and we should always assess the performance of =
the combination of "model(s) + potential true model parameters sets + =
design" for both efficacy and safety endpoints. This is always =
enlightening, and often reveals why Phase 2 studies do need to be =
large...getting high levels of precision on the D-E-R is hard, even when =
we use all the data! To minimise N (and maximise information), the =
design should be adaptive. We should learn as we go, and target those =
dose levels which teach us most about the D-E-R for both efficacy and =
safety endpoints as we accrue data during the study. Safety D-E-R should =
not be a post-hoc (and weak) secondary analysis. A good example using =
multiple efficacy and safety parameters to adaptively find doses with =
potentially maximum utility in Phase 2 is worth reading (2), even =
though we may not love the models and subsequent dose selection methods =
therein for the Phase 3 part. This is one example (adaptive design) =
where having an initial simpler D-R model will be helpful for the dose =
adaptations, since it may be logistically challenging to get PK =
information in real time. A criticism of MCP-MOD is that if you wish to =
entertain models like the linear model at the analysis stage, you should =
also design/optimise your study around these models. Since I have no =
desire to fit a linear model, I can happily ignore it at the design =
stage, and focus on designs which will do well over a set of plausible =
sigmoidal Emax type models.

d) The ability of the design/data to detect a D-E-R relationship

The MCP part of MCP-MOD is concerned with being able to reject the "no =
D-E-R" null hypothesis. Like the Power to detect a given treatment =
effect, we can indeed discuss the power to detect a given D-E-R, but it =
is often quite pointless. Crudely, we could say we have detected a D-E-R =
if the 95% CI for Emax does not include zero, but this result is wholly =
useless from a prediction perspective, since our D-E-R predictions will =
range from a lot to near zero. That is, standard "powered" phase 2 =
studies do not ensure useful predictions can be made. Thus the N =
required to obtain a reasonably high precision on the D-E-R is MUCH =
higher than that needed to detect the D-E-R. In short, if we are trying =
to work out if the D-E-R is not flat at the final analysis stage, the =
design was probably flawed (or we should have stopped for futility a =
while ago).

e) The presentation of multiple credible models, and possible model =
averaging.

Whilst I am not against using Bayesian model averaging per se, I think =
the individual results for each credible model should be presented =
simultaneously, to see if any key decisions (e.g. dose selections for =
phase 3) are dependent on the choice of model (...think of a forest =
plot). Clearly we hope they are not, but when they are, we need to know =
that, since we may wish to dig further and/or make decisions that =
reflect our uncertainty (rather than simply presenting an "average" =
effect). Of note, clearly the model set being combined is key (e.g. if =
it is a set of PKPD models which differ only in covariate effects, then =
the results may be all very similar, whilst structurally different =
models from separate modelling groups (e.g. pharmacometrics, stats, =
system pharmacologists) may yield a much wider distribution of =
predictions.


I'll stop there.


Nele...if you feel any of your original questions remain unanswered, =
feel free to give me a call.


Kind regards,

Al


(1) see "https://www.youtube.com/watch?v=E713BehI2fE)"
(2) See "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570871/" and =
related papers


Al Maloney
Consultant Pharmacometrician

Phone: +46 35 10 39 78
E-mail:al.maloney_at_astrazeneca.com
E-mail:al_in_sweden_at_hotmail.com






On 20 Mar 2015, at 13:01, Mueller-Plock, Nele wrote:

> 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
> 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
> http://www.takeda.com
>
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Received on Fri Mar 27 2015 - 05:23:33 EDT

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