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. _____________________________________________________________________________________________ AstraZeneca Innovative Medicines | Quantitative Clinical Pharmacology SE-431 83 Mölndal, Sweden T: +46 (0)31 776 23 41 Mob: +46 (0)708 467 667 [email protected] Please consider the environment before printing this e-mail 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 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: [email protected]<mailto:[email protected]> http://www.takeda.com<http://www.takeda.com/> -------------------------------------------------------------------- The content of this email and of any files transmitted may contain confidential, proprietary or legally privileged information and is intended solely for the use of the person/s or entity/ies to whom it is addressed. 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