A Method That Could Revolutionize How Trial Outcomes Are Analyzed

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: Looking ahead to the rest of the year, what changes in data science and trial complexity will most impact how clinical trials are designed and executed?

Buyse: Okay, I'm going to give you a very biased answer—take it with a grain of salt. My colleagues and I have been working for years on a method we believe has huge potential to completely revolutionize the way we analyze and design trials. We call it generalized pairwise comparisons, and it allows you to put more than one outcome into the primary analysis of a trial—not just one criterion, as has typically been done in the past.

There's a special case of this method called the win ratio, which has gotten a lot of traction in cardiovascular disease. Instead of looking at just one event and the time to that event, you can look at several. A patient might die, or they might have a myocardial infarction, or a heart attack, or bleeding—a number of events. So instead of looking at the time to first event, which is the traditional approach, you use a method that looks at the time to the worst event first, and then if the patient hasn't had that event, you look at the time to the next bad event, and so on. Hierarchical, or prioritized, outcomes—all analyzed together in a single analysis with a single measure of treatment effect.

The win ratio is what's used in cardiovascular disease. We propose another measure called the net treatment benefit, because it incorporates data from all of these outcomes simultaneously. Think of the power you can get—you can vastly increase the power of an analysis because every outcome contributes.

And because you look at all the outcomes, it's also far more patient relevant. If you tell a patient the only thing that will decide if this trial succeeds or fails is survival time, the patient asks: what about quality of life? What about other events I don't want to have? And you have to tell them that those will only be looked at if survival improves. That doesn't make sense to the patient. It doesn't make sense to scientists either.

So we need to evolve. And I believe personally that this method will really revolutionize how we look at things — though again, I'm biased. We work on it, we fell in love with it, and we're convinced it can change clinical research fundamentally. Take it for what it's worth.