Best practice in modelling and simulation: initiatives from PSI and EFPIA
Michael O’Kelly (Quintiles)
At an EMA-IFPIA workshop on modelling and simulation in 2011, regulators called for a Best Practice document for projects involving modelling and simulation. Finally, in the last few months, two different working groups have come up with two tools aimed at helping statisticians and their colleagues to promote and use best practice. The European Federation of Pharmaceutical Industries & Associations (EFPIA) working group on Model Informed Drug Discovery and Development (MID3) has just published a wide-ranging 90-page paper on the whys and hows of Best Practice. The paper includes as a supplement a table of 103 examples of the use of modelling and simulation in pharmaceutical science. At the same time, the Board of PSI in December adopted a Best Practice document proposed by the PSI Modelling and Simulation Special Interest Group (SIG). The two initiatives are consistent in their recommendations, with the SIG providing a template that could be used to put the recommendations for best practice into action. This presentation will identify the elements necessary for best practice in simulation of clinical trials, and survey current practice in this area.
Writing Clinical Trial Simulators: there’s more to it than the stats analysis.
Tom Parke (Berry Consultants)
When writing ones first trial simulator it’s inevitable that one’s focus (as a stats programmer) is on the novel aspect of the trial design that has required writing a simulator in the first place. However there are other aspects of trial simulation that will end up as being every bit as important. By understanding these other aspects in advance you’ll be able to better plan to accommodate them and do a better job of writing and using a trial simulator.
Assessment of various continual reassessment method models for dose-escalation phase 1 oncology clinical trials: Clinical trial data and simulation studies
Gareth James (Phastar)
Background: The continual reassessment method (CRM) is considered more efficient and ethical than standard methods for dose-escalation trials in oncology, but requires an underlying estimate of the dose-toxicity relationship (“prior skeleton”) and there is limited guidance of what this should be when little is known about this association.
Aim: To compare the CRM with different prior skeleton approaches and the 3+3 method in their ability to determine the true maximum tolerated dose (MTD) of various “true” dose-toxicity relationships.
Simulating Dose Finding Designs using Bayesian Decisions, with examples for Severe Asthma, Ulcerative Colitis and Alzheimer’s Disease.
Alun Bedding (AZ)
Choosing the correct dose for Phase III is a pivotal part of drug development. Model based approaches are the most appropriate way of doing this and Bayesian decisions are more informative. Simulation is needed in order to understand the operating characteristics of designs given different assumptions. Examples, including those for severe asthma, ulcerative colitis and Alzheimer’s disease will be used for illustration.
In clinical trial design we often use simulations to illustrate operational characteristics where analytical solutions are intractable. In this talk we will look at some case studies where these simulations have aided decision making and so increased understanding within the project team. We will look at an example of complex go/no go criteria and how these criteria were refined based on the results of the simulation. We will look at an example of how simulations were used to explore the impact of partial data on the operational characteristics of a futility analysis.
Simulating a sequences of clinical trials - a whole drug development program, in order to optimize the design, planning and resource allocation
Tom Parke (Berry Consultants)
It’s hard to judge how big a phase 2 should be, what the trade off in power and type error should be, and what the significance of other post phase 2 errors (such dose selection, population selection, estimate of effect size) might be, without considering the whole picture of time to registration, overall probability of technical success and overall value / clinical usefulness should the treatment be successfully registered.
PSI, the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) and the Biopharmaceutical Section of the American Statistical Association (ASA) are jointly organising a webinar on Estimands in Practice. Speakers from regulatory authorities (FDA and EMA) and industry will present on their experience on this topic to date.
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