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And if you’re a Bayesian, you can choose a stopping criterion based on your desired degree of certainty. This can have practical benefits of not having to run a

And if you’re a Bayesian, you can choose a stopping criterion based on your desired degree of certainty. This can have practical benefits of not having to run an experiment to its end. A family member was part of the control group for a cancer treatment study a few years back. The study chose a stopping criterion based on Bayesian methods. Relatively early into the study they were able to determine that it made sense to move people from the control group to the treatment group.

icegreentea2 6y ago
You don't have to be a bayesian to do that. Once you're dealing with real people, early termination is a huge deal, and it quickly goes beyond just the interpretation of statistics. You almost always want to have 3rd party referees involved anyways.
- MiroF 6y ago
  > 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.
  - mattkrause 6y ago
    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.
    - icegreentea2 6y ago
      Yes, I meant for clinical trials. Agreed that things are weirder for basic research.
deleted 6y ago
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icegreentea2 6y ago

You don't have to be a bayesian to do that. Once you're dealing with real people, early termination is a huge deal, and it quickly goes beyond just the interpretation of statistics. You almost always want to have 3rd party referees involved anyways.

MiroF 6y ago
> 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.
- mattkrause 6y ago
  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.
  - icegreentea2 6y ago
    Yes, I meant for clinical trials. Agreed that things are weirder for basic research.

deleted 6y ago

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