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Robust Trial Design Under Treatment and Enrollment Uncertainty

The planning and optimization of a clinical trial is beset by uncertainties: knowledge of treatment effects, the selection of patient population, choice of endpoints, even recruitment uncertainties can assail the typical decision-maker. In a recent Cytel webinar, Dr. Pantelis Vlachos argued that with access to the right historical data, uncertainty can transform into robust clinical trial design. Using a Phase III double-blind multicenter trial, Dr. Vlachos showed how new technology can be harnessed to answer an array of questions about early stopping, the pacing of interim analyses, enrollment variability and cost-optimization.

Setting the Scene:

Dr. Vlachos began with a concrete mandate: how to reduce overall trial duration while maintaining power at 85% or more.

He then added in a number of more granular questions:

  • What is the optimal spacing of interim analyses to achieve this aim?
  • Should early stopping for futility or efficacy be considered?
  • What are the tradeoffs between sample size and duration?
  • How will enrollment and treatment effect variations affect the trial?

Using Cytel’s Solara, Dr. Vlachos quickly determined that 133 possible designs should be considered for this particular clinical trial.

Handling Unknowns:

For specific assumptions about hazard rates, estimated overall survival times and enrollment rates, optimizing 133 possible designs would be difficult. Yet the reality was, there were still uncertainties about all three of these variables.

One of the more complex calculations in this process involved modeling fluctuations in enrollment rates. The study was conducted across 121 sites spread over 14 countries. Each country experienced seasonal fluctuations in recruitment.

Using Cytel’s Enforesys, Dr. Vlachos was able to use site-level historical data to model slow and fast enrollment projections. He was able to quantify uncertainties and account for fluctuations in recruitment cycles, thereby informing the scenarios under which the optimization above had to take place.

Simulation for Optimization:

After accounting for 6 different hazard ratios, 3 different survival times and fluctuations in enrollment rate, Dr. Vlachos confronted 111 different potential scenarios depending on these three variables.
It took Dr. Vlachos about 20 minutes to simulate all of these designs across the various different combinations of hazard ratios, estimated overall survival times and enrollment fluctuations. Solara(R) performed over 14.7 million simulations in that time, and chose 3 designs: one which optimized for power, one for speed and one for power.

To learn more about this case study, click below:

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About the Author of Blog: 

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Dr. Esha Senchaudhuri is a research and communications specialist, committed to helping scholars and scientists translate their research findings to public and private sector executives. At Cytel Esha leads content strategy and content production across the company's five business units. She received a doctorate from the London School of Economics in philosophy, and is a former early-career policy fellow of the American Academy of Arts and Sciences. She has taught medical ethics at the Harvard School of Public Health (TH Chan School), and sits on the Steering Committee of the Society for Women in Philosophy's Eastern Division, which is responsible for awarding the Distinguished Woman in Philosophy Award.