{"id":3220,"date":"2024-06-10T11:22:34","date_gmt":"2024-06-10T15:22:34","guid":{"rendered":"https:\/\/cytel.agencyukdev.com\/perspectives\/managing-uncertainty-simulation-based-assurance-in-clinical-trial-design\/"},"modified":"2024-08-22T05:49:00","modified_gmt":"2024-08-22T09:49:00","slug":"managing-uncertainty-simulation-based-assurance-in-clinical-trial-design","status":"publish","type":"perspectives","link":"https:\/\/cytel.agencyukdev.com\/fr\/perspectives\/managing-uncertainty-simulation-based-assurance-in-clinical-trial-design\/","title":{"rendered":"Managing Uncertainty: Simulation-Based Assurance in Clinical Trial Design"},"content":{"rendered":"

The past two decades have seen the adoption of great innovation in clinical trial design. Statisticians have risen to the challenge by proposing a myriad of solutions to the ever more complex questions being posed by the trial team. Bayesian statistical techniques have been key among the various methodologies proposed in solving difficult problems, but only recently have they come into their own as computation and simulation power have matured.<\/p>\n

One of the chief concepts at the heart of Bayesian inference is managing uncertainty, which, in many regards, is the key to success in clinical development. A lengthy, expensive, and risky process, bringing a new drug to market is a high-stakes endeavor. Any ability to characterize and mitigate risk is therefore welcome. Bayesian statistical inference, relying on a solidly probabilistic framework, can help by making straightforward risk assessment statements to clinical and business questions.<\/p>\n

One of the questions clinical teams routinely ask when designing a clinical trial is \u201cwhat is the chance that I declare the trial successful when in fact a treatment effect does exist?\u201d The answer to this question has traditionally been provided as the power of a study. Calculating power relies on making a strong assumption about what fixed value the treatment effect takes. In reality, we don\u2019t know what the treatment effect will be, and it is very difficult to assign a single value a priori to feed into this power calculation. Inevitably, there is uncertainty in this assumption and were we able to capture it somehow, we could give a better answer to our question.<\/p>\n

As you might suspect, it is possible to do this. Given an assumed distribution for the treatment effect, one can compute power under all possible values of the treatment effect and summarize this in a value called assurance<\/strong>1<\/span><\/sup><\/a> that better reflects our underlying uncertainty at the start of the trial. Assurance can sometimes easily be computed, but not always, and it is just as easy to simulate it given today\u2019s cheap computational power.<\/p>\n

Why is assurance a better measure of success than power? The answer is simple. It accounts for the uncertainty in our assumptions when designing a trial. It therefore gives a more accurate measure of the risk in the experiment we are about to engage in and does a better job of informing clinical development planning, funding, and go\/no-go decisions.<\/p>\n

 <\/p>\n

Interested in learning more? Watch for our webinar<\/a>, “Managing Uncertainty in Trial Design: A Deep Dive into Simulation-Based Assurance with Solara\u00ae”<\/p>\n

 <\/p>\n

Note<\/span><\/p>\n

1. Anthony O’Hagan, John W. Stevens, and Michael J. Campbell, \u201cAssurance in Clinical Trial Design<\/a>,\u201d Pharmaceutical Statistics<\/em> 4 (3) (2005).<\/p>\n

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