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Evolving the Study Design Process: An ACT Webcast by Dr. Yannis Jemiai

There are many reasons why traditional approaches to designing a clinical study are generally suboptimal and do not generate the most efficient designs. A lot of them are connected to uncertain inputs and lack of sufficient tools. Study design input estimates such as, treatment effect, enrollment rate etc., in traditional studies are prone to error and bias. The process itself is fragmented, siloed and iterative and can take months.

Additionally, it is a challenge to get multiple stakeholders including regulatory, medical, clinical, statistical, clinical operations and market access to come together and look at the aim of the study and the business objectives of the sponsoring organization and find the optimal design options. Frequently, due to lack of time it is only possible to look at a small set of design options to pick the best design for your study. This does not guarantee that you have you have come up with the best design, considering the wide variety of possibilities that are available.

For a faster, more efficient path to patients for new therapies, an improved trial design and selection process is necessary. This will ensure consideration of all relevant design options, the ability to understand design trade-offs and streamline the sequential iterative process of design.

Clinical Trial Strategy: Essentials

The first step in this process is to build an expansive design space, for which you need simulation technology. Biostatisticians wielding the latest technology can apply massive cloud computing power to thoroughly map the entire relevant design space in minutes, thereby surfacing better opportunities to meet development goals.

For example, Solara, Cytel’s innovative trial strategy platform, can combines massive cloud compute with Cytel algorithms to dramatically expand available design options for confident selection of the optimal design. Recently, Cytel worked with a biotech sponsor to identify the optimal design options requiring the least number of sites and patients with the highest probability of success. Our statistical experts simulated 59 million trials for the sponsor in less than 30 minutes. Study designs that have been optimized using Solara generate, on average, 10-20% savings in speed, cost, or probability of success. These improvements are driven by the identification of improved combinations of design variables that would rarely have been found through manual exploration.

In a recent webcast with Applied Clinical Trials, Yannis Jemiai, Cytel's Chief Science Officer reveals how trial sponsors can leverage Solara to quickly explore millions of trial designs through advanced simulation methods and answer key clinical strategy questions.

Quantitative Approaches

The current decision-making process is relatively ad hoc and all the information assembled is usually fragmented and unstructured. There is no transparency on how the options that are presented to the decision-makers came about. We are now trying to evolve the decision-making process by applying decision analytical frameworks where quantitative data and qualitative data are combined and presented to the decision maker or decision-making committee. This provides an assurance that a rigorous process has been conducted and they are provided full transparency with regards to the associated risks and return on investment.

In the webinar, Yannis explains three approaches to finding the optimal design:

1. Financial return on investment: In this approach, the key objective is to maximize the expected Net Present Value (eNPV), a measure that appropriately discounts future return on investment while weighing the probability of its realization. The way you design a study can significantly impact return on investment. The eNPV maximized designs have a rigorous business objectives driven approach to decision making.

2. Multi-criteria decision analysis: This approach relies on scoring and ranking all designs to optimize for a company’s scientific and business goals. It is particularly suited for interventions where multiple, sometimes conflicting, criteria play a role, and the viewpoints of multiple stakeholders about the importance of decision-criteria need to be considered.

3. Game Theory based optimization: The Pareto Frontier is a concept from economic choice theory, which mathematically represents the set of preferences where improvement along one parameter is not possible without reduction along another. Taken individually, each point on the Pareto Frontier is Pareto Optimal. Cytel has adjusted this concept for the clinical development space specifically, reflecting points where sponsors can be sure that for any given investment in cost and time, the maximum possible power is available.

To learn more, click the button to watch the on-demand webcast.

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About the Author of Blog:

Mansha Sachdev specializes in content creation and knowledge management. She holds an MBA degree and has 11 years of experience in handling various facets of marketing, across industries. At Cytel, Mansha is a Content Marketing Manager and is responsible for producing informative content that is related to the pharmaceutical and medical devices industries.