A Data-Infused Approach to De-Risking Clinical Trials
For many decades the Pareto Frontier has been employed by actors in the private sector to evaluate and understand the benefits of various strategic options. When trying to understand the risks and benefits of a clinical strategy, Cytel researchers urge using a modified version of this process, built primarily with clinical development in mind. Their new position paper describes two functional uses of the Pareto concept for clinical trials - the selection of a de-risked trial design, and an improved understanding of the financial nature of the tradeoffs between various operational parameters of clinical trial design.
Have you ever encountered the following when trying to select an optimal trial design:
- You have a reasonably good design, but have no way of telling whether a better one exists given your upfront investments?
- You have more than one design and have no way to calculate the pros and cons of the various tradeoffs?
- You wonder if there is an unknown design that would be easier to implement and which you are overlooking?
Most of the time, when statisticians say they have found an optimal trial design, they are optimizing within a constrained set of clinical trials. They might have a sample size re-estimation design, a group sequential design and a traditional trial design in front of them, and are optimizing these along features like power of a study, savings, expected net present value, or other parameters.
Now there are some clinical trial strategy platforms, like Cytel's Solara, that might design more than a few dozen options. Solara, for example, might be able to provide over a thousand designs across millions of design scenarios within a few minutes. Yet this changes the decision problem, but how does it clarify options?
For one, being able to design millions of options within minutes, means a sponsor must quickly identify which of the designs to evaluate. A Pareto Frontier can help sponsors identify this set. Every design on the Pareto Frontier represents one where a sponsor cannot make improvements along one operational parameter without creating a diminishing along another. Every clinical trial design on the Pareto Frontier is optimized across a set of parameters.
Secondly, a Scoring Function is then available within Solara to rank-order the designs on the Pareto Frontier using sponsor-specific considerations. For example, if a sponsor has a competitor about to enter a market, and wants to accelerate a clinical trial without compromising power, the Scoring Function can select the clinical trial designs that enable a sponsor to do so. If another sponsor has fewer concerns about the speed of a clinical trial, but might be facing constraints on sample size, the Scoring Algorithm can accommodate this feature of the sponsor's clinical strategy without compromising study power.
The result is a clearer and more data-enriched approach to clinical strategy. To learn more about using the Pareto concept in clinical strategy and decision-making download this complimentary whitepaper from Cytel.
About the Author of Blog:
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 Eastern Division of the Society for Women in Philosophy which is responsible for awarding the Distinguished Woman Philosopher Award.