For over a decade, advanced trial design techniques have promised efficient trials with accelerated timelines, reflecting the ability to quantify uncertainty and de-risk trials using adaptive tools. Despite the emergence of these complex innovative designs, the success of Phase 3 trials has continued to hover at 33% while the average time to market remains about 6 years.
While the number of trial designs and types have gently expanded, finding the optimal trial design for a specific context remains an elusive goal. Typically, a sponsor’s R&D team will identify five or six designs that are sent to a statistician. Once the statistician designs these trials, the most promising designs are optimized. The entire process can typically take several weeks.
Given that there exist tens of thousands of potential designs, beginning with half a dozen potential trials and then making incremental design changes is unlikely to locate the ideal trial for a specific situation. Rather, trial sponsors appear to be engaged in what some social psychologists call satisficing. They identify five or six designs that satisfy some basic requirements, and with minor modifications optimize across this constrained subset. Finding the optimal trial design requires optimizing across every potential trial.
Cloud-computing has altered the process of trial design, by taking familiar techniques for simulation and modeling, and generating thousands of different trial designs. Cytel’s Solara, for example, can generate in 30 minutes what it once took 500 computers to design after many hours. Ironically, this has the potential to lead to a different kind of satisficing. Given that statisticians are now working with over 100,000 different designs, finding the optimal across these can take effort. Further, discussing tradeoffs among all of these potential designs can be complicated, as different business units might have different stakes in the trial implementation process, and communicating the nuances of these values in the language of probabilistic tradeoffs can create confusion.
Cytel’s thought-leaders have spent over a decade perfecting an Advanced Design Framework to enable trial sponsors to overcome the challenges of satisficing, and channel trial designs towards their global optimum. Over the next several weeks, Cytel will be revealing the three elements of this Advanced Design Framework which will enable R&D teams to Thoroughly Explore the relevant statistical design space, Decide Together across business units, and Communicate Trade-offs more clearly.
Through the use of new design processes as well as new technology, we will discuss how to create a new strategic mindset that engages statisticians earlier and unlocks their fuller potential to help guide strategic decisions for commercial value. Early results of this new method demonstrate 10%-20% increases in productivity, higher expected net present value, and development timelines reduced by several weeks.
Read the first part of a three parts blog series on Cytel's Advanced Design Framework here.
Cytel recently released Solara™, a collaborative decision-support platform that unifies statistics and strategy to optimize clinical trial design. Contact us to learn more.
Ma, Jingjing, and Neal J. Roese. "The maximizing mind-set." Journal of Consumer Research 41.1 (2014): 71-92.