Imagine you're running a clinical trial to test a new treatment in critical care. You're tracking how patients do after the treatment, but what if some patients don't make it long enough to see the full results? That's what we're calling "truncation due to death. " The big question is, how do we handle this situation? We don't really know what methods are best for this, so a group of experts got together to figure it out. They used something called the Delphi method, which is a way to reach a consensus on a topic by asking experts to answer questions and then refining their answers.

One of the main issues is that the definition of what the treatment effect is (called the estimand) and how we analyze the data statistically need to take this into account. It's not just about counting how many patients died or lived; it's more complicated than that. The experts discussed different ways to approach this, like using statistical models that can handle missing data or focusing on patients who survived long enough to see the full effect.

Some approaches might be more important to certain people, like doctors or patients, while others might be more relevant to researchers or policy-makers. The goal was to figure out what approaches are most relevant to everyone, so we can make sure clinical trials are fair and accurate.

Dealing with Death in Clinical Trials: What Matters Most?

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