> You don't have to be a bayesian to do that.

The reasoning for doing so is much more coherent (imo) in a Bayesian framework where you don't have to say that you are "going beyond" statistics.

By "beyond statistics", I think they meant that trials need on-going monitoring for things beyond the outcome variable itself: safety, data quality, and even feasibility of completing the trial itself. Once you've got that infrastructure in place, interim monitoring of the outcome isn't much extra work.

Some of the frequentist approaches to early-stopping seem pretty coherent to me. Curtailiment, for example, just stops collecting data once it will no longer change the outcome of a test. The frequentist emphasis on error control for the procedure also seems like a reasonable fit for a regulatory regime where you're actually making decisions (no argument that it is...weirder for basic research)

At any rate, some approaches for early-stopping have sensible frequentist and Bayesian properties, which is nice.

Yes, I meant for clinical trials.

Agreed that things are weirder for basic research.