An interim analysis of a clinical trial is an unusual analysis. At the end of the trial you want to estimate how well some treatment X works. For example, you want to how likely is it that treatment X works better than the control treatment Y. But in the middle of the trial you want to know something more subtle.

It’s possible that treatment X is doing so poorly that you want to end the trial without going any further. It’s also possible that X is doing so well that you want to end the trial early. Both of these are rare. Most of the time an interim analysis is more concerned with futility. You might want to stop the trial early not because the results are really good, or really bad, but because the results are really mediocre! That is, treatments X and Y are performing so similarly that you’re afraid that you won’t be able to declare one or the other better.

Maybe treatment X is doing a little better than Y, but not so much better that you can declare with confidence that X is better. You might want to stop for futility if you project that not only do you not have enough evidence now, you don’t believe you will have enough evidence by the end of the trial.

Futility analysis is more about resources than ethics. If X is doing poorly, ethics might dictate that you stop giving X to patients so you stop early. If X is doing spectacularly well, ethics might dictate that you stop giving the control treatment, if there is an active control. But if X is doing so-so, there’s usually not an ethical reason to stop, unless X is worse than Y on some secondary criteria, such as having worse side effects. You want to end futile studies so you can save resources and get on with the next study, and you could argue that’s an ethical consideration, though less direct.

Futility analysis isn’t about your current estimate of effectiveness. It’s about what you think you’re estimate regard effectiveness in the future. That is, it’s a second order prediction. You’re trying to understand the effectiveness of the trial, not of the treatment per se. You’re not trying to estimate a parameter, for example, but trying to estimate what range of estimates you’re likely to make.

This is why predictive probability is natural for interim analysis. You’re trying to predict outcomes, not parameters. (This is subtle: you’re trying to estimate the probability of outcomes that lead to certain estimates of parameters, namely those that allow you to reach a conclusion with pre-specified significance.)

Predictive probability is a Bayesian concept, but it is useful in analyzing frequentist trial designs. You may have frequentist conclusion criteria, such as a p-value threshold or some requirements on a confidence interval, but you want to know how likely it is that if the trial continues, you’ll see data that lead to meeting your criteria. In that case you want to compute the (Bayesian) predictive probability of meeting your frequentist criteria.