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Cytel Introduces Advanced Design Framework: Part 3 - Communication Techniques to Ensure Alignment on Data-Driven Clinical Trial Designs

Cytel has recently revealed its Advanced Design Framework, a method developed by Cytel’s thought leaders that draws on decades of experience increasing clinical development productivity. The Framework illustrates how advances in design processes and technology can help development teams deliver greater business results, unifying statistics and strategy in the era of cloud computing and making strategic use of well-resourced statisticians.

The framework consists of three parts: Thoroughly Explore, Decide Together, and Communicate Tradeoffs. In this blog we take a deeper look into the third part of this Framework, revealing how clear articulation of the chosen design’s benefits and drawbacks can help ensure data-driven decision-making that improves speed, savings and success.

The stakes are high in pharmaceutical development, and selection of the statistical trial design often requires several layers of approval before a team has authorization to proceed. These debates often revisit the specific contextual strategies necessary to make a new therapy, device or biologic commercially viable. Given the constantly changing conditions of the pharmaceutical development industry, this often requires intense and nuanced conversations about the distribution and availability of resources, the situation of competitors, and the long-term goals of a corporate portfolio.

Unfortunately, incomplete information, misinformation or bias during these preliminary discussions can have long-term consequences. Sponsors report delayed trials, repeated governance board meetings, mistaken projections regarding timelines and revenue, and sub-optimal trial designs all resulting from misunderstandings, misinformation and undue deference to authority figures during the first few R&D meetings.

Data-driven decision-making means recognizing and confronting trial design outcomes that result from:

  • Eminence-based rather than evidence-based decision-making (e.g. deference to clinicians, scientists, and other executives rather than the evidence at hand)
  • The improper quantification of operational characteristics like forecasted timelines, enrollment targets, trial sites and so forth
  • The complex challenge of communicating statistical insights about trial risk and articulating the commercial implications of not pursuing sound risk-mitigation
  • Creating governance reports that accurately reflect all strategic options and are easy to update in light of new evidence.

The drawbacks to not rigorously challenging these practices can result in lower probability of success, inefficient and possibly unethical uses of patients, missed opportunities for scientific progress, and millions of dollars in lost revenue. Changing norms, though, requires a multitiered approach to change.

1: Strategic and Tactical Uses of Well-Resourced Biostatisticians

2: Communicate a Decision-Framework

3: Clearly Articulate Value of a Design and Be Ready to Iterate

 

1: Strategic and Tactical Uses of Well-Resourced Biostatisticians

Nowadays, biostatisticians have access to tools and technology not available to their counterparts even five years ago. Simulations that offer more precise forecasting, combined with the ability to create exponentially more designs in minimal timeframes, are hallmarks of the cloud-computing era. A statistician equipped with Cytel’s Solara platform, for example, can simulate 100,000 trial designs within 30 minutes. A statistician using R-code five years ago would have taken three days to design 5 or 6 trials. This means statisticians can explore a much wider terrain of design possibilities, within far less time.

Yet rather than simply having statisticians design 100,000 potential trials, R&D teams should also consider communicating commercial opportunities and industry challenges to statisticians. Amongst 100,000 designed trials there might be a few hundred unexpected opportunities where R&D teams can harness risks and channel uncertainties towards favorable commercial goals. Statisticians who know the opportunities to be sought will also know to be on the lookout for these possibilities, whether it be accelerated timelines, higher probability of success, or earliest possible stopping for trials not likely to succeed.

2: Communicate a Decision-Framework

A decision-framework is a set of rules that explicitly articulates how different elements of trial design should be weighted. Some trials might need accelerated timelines due to a competitor nearing completion of a trial, and a slightly lower probability of success is worth the risk of completing a year early. Other trials might want higher probability of success, and therefore need extra time to acquire time-to-event endpoints. A decision-framework should stipulate how different trial parameters will be measured so that tradeoffs are clearly and adequately prioritized.

3: Clearly Articulate Value of a Design and Be Ready to Iterate

When engaged in highly technical discussions, particularly about the quantification of risks, the optimization of opportunities across many parameters, and highly nuanced tradeoffs, it is easy to misunderstand what the evidence is really saying. Use visual communication tools to graphically represent tradeoffs, and leverage software that can give feedback in real time. This ensures that every member of the team understands the decision problem and the data being used to resolve challenges.

Read the first and second part of this blog series.


 

Join the first webinar in our C-Suite Webinar Series to learn how preliminary testing of Cytel’s Advanced Design Framework has consistently identified opportunities to increase clinical development productivity 10-20%.

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Cytel recently released Solara™, a collaborative decision-support platform that unifies statistics and strategy to optimize clinical trial design. Contact us to learn more.

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