How Sponsor Expectations Are Reshaping Data Strategy and Trial Design

In a recent video interview with Applied Clinical Trials, Marc Buyse, ScD, founder and CEO of IDDI, discussed how growing trial complexity is creating new challenges for data confidence and interpretability, emphasizing that transparency and reproducibility must remain non-negotiable as Bayesian and adaptive designs become more common. He highlighted key operational gaps—including opaque methodologies, unchecked reliance on synthetic controls, and the risks of black-box analytical approaches—while making a strong case for preserving the randomized controlled trial as the gold standard wherever feasible. Buyse also reflected on the lessons of the COVID era, arguing that the clinical trial enterprise needs to become dramatically more pragmatic and cost-efficient, not just statistically sophisticated. He stressed the critical role of early planning and the estimand framework in anticipating and managing trial conduct problems, and closed with an enthusiastic case for generalized pairwise comparisons and the win ratio as methods he believes will fundamentally reshape how trial outcomes are analyzed and interpreted.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

ACT: How are sponsor expectations evolving when it comes to data strategy, analytics, and regulatory readiness?

Buyse: Well, expectations are a huge problem, because people have high expectations. They expect a kind of magic that will make their drug convincingly efficacious with much less information than ever before. And that is not true. As a statistician, I have to say that the truth is in the data, and only in the data—not in what you think, not in what you hope.

All sponsors do a trial with the hope that their drug will work. We all hope that's true. But the reality is that some drugs don't work, or don't work as well as expected. The proof of the pudding is in the eating. You have to have enough data to support a claim.

I think the covid experience was really an eye opener in many ways. In less than no time, there were large, pragmatic trials that showed which treatments were active against covid and which were not. The data was so solid that it contradicted what people believed. Dexamethasone was found to be quite efficacious in hospitalized patients—that was not anticipated. Hydroxychloroquine, which had been widely claimed to be very efficacious based on real world evidence, was shown to have no efficacy whatsoever. And you may remember all the hype about hydroxychloroquine—even the President of the United States was claiming you should use it. It just didn't work. It's not an active drug.

What was very peculiar during covid is that all the heavy procedures we normally use to run a trial—the IRB, submitting the protocol, opening sites, all the due diligence—were lifted because of the urgency. And so you might wonder: why was that possible then, but now it's become very tedious again? The common diseases we suffer from today kill far more people than all the covid deaths combined. So why do we live in a world where a clinical trial is hugely expensive and hugely complicated, when during covid all of those complications went away overnight?

I think we need to focus on the reality of clinical trials. Why is it that less than 5% of all patients are ever offered to go into a randomized trial? In the United Kingdom, 15% or more go into clinical trials, because they have a national strategy for entering patients into trials far more aggressively than we do in continental Europe or the United States. That's something we should reflect on.

It's not just about having smart theories and smart statistical techniques. We need to become much smarter in running trials—keeping the good old principles, but making trials more pragmatic, more cost effective, and far less costly. Risk-based monitoring is one example of a potentially huge cost saver, because it costs a lot of money for human beings to check clinical trial data, and human beings don't do it that well. The company I co-founded, CluePoints, does this automatically based on statistical methods and now AI as well. That's the kind of thinking we need—not reducing costs by 30% or 50%, but by a factor of 10, 20, or even 100, because we spend too much money on things that don't matter in trials.