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

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/WC500161028.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/2015/01/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:[email protected] E-mail:[email protected]