It's an FDA guidance doc so you can expect to see this affecting new filings or supplemental filings for upcoming FDA submissions and clinical trial designs. This is good news, statistical plans are always a point of contention during trial design and the submission process. This guidance lays a line in the sand and will remove some of the reviewer-to-reviewer variance present in the current FDA staff.
> This guidance lays a line in the sand and will remove some of the reviewer-to-reviewer variance present in the current FDA staff.
That would be nice, but my experience is there can be quite significant variability between reviewers in different teams/groups, even on topics you'd think were well-established for many years, and for which there is existing FDA guidance.
Accepting Bayesian methods for RCTs is great news and leading biostatisticians like Frank Harrell have been pushing for this change for many years. What I'm most interested to see: will this actually be implemented in practice, or will it be incredibly rare and niche, like Bayesian methods are currently in most biomedical fields?
Regulators are generally really conservative. Spiegelhalter et al. already wrote a fantastic textbook on Bayesian methods for trial analysis back in 2004. It is a great synthesis, and used by statisticians from other fields. I have seen it quoted in e.g. DeepMind presentations.
Bayesian methods enable using prior information and fancy adaptive trial designs, which have the potential to make drug development much cheaper. It's also easier to factor in utility functions and look at cost:benefit. But things move slowly.
They are used in some trials, but not the norm, and require rowing against the stream. This is actually a great niche for a startup. Leveraging prior knowledge to make target discovery, pre-clinical, and clinical trials more adaptive and efficient.
Journals are also conservative. But Bayesian methods are not that niche anymore. Even mainstream journals such as Nature or Nature Genetics include Bayesian-specific items in their standard submission checklists [1]. For example, they require you to indicate prior choice and MCMC parameters.
Bayesian methods are incredibly canonical in most fields I’ve been involved with (cosmology is one of the most beautiful paradises for someone looking for maybe the coolest club of Bayesian applications). I’m surprised there are still holdouts, especially in fields where the stakes are so high. There are also plenty of blog articles and classroom lessons about how frequentist trial designs kill people: if you are not allowed to deviate from your experiment design but you already have enough evidence to form a strong belief about which treatment is better, is that unethical? Maybe the reality is a bit less simplistic but ive seen many instantiations of that argument around.
If choosing a Bayesian approach in a clinical trial can reduce the number of recruited subjects, I would imagine the pharma industry is strongly incentivized to adopt it.
Moreover you can manipulate your results by disingenuous prior choices, and the smaller sample you have the stronger this effect is. I am not sold on the FDA's ability to objectively and carefully review Bayesian research designs, especially given the current administration's wanton disregard for the public good.
The application of Bayesian probabilistic reasoning in general (as described in this video) is not the same thing as "Bayesian statistics" specifically, which usually to modeling and posterior inference using both a likelihood model and a prior model. It's a very different approach to statistical inference both in theory and in practice. This creator himself is either ignorant of this distinction or is trying to mislead his viewers in order to dunk on the FDA. It's obvious from the video comments that many people have indeed been misled as to what Bayesian statistics is and what the implications of its might be in the context of clinical trials.
It's an FDA guidance doc so you can expect to see this affecting new filings or supplemental filings for upcoming FDA submissions and clinical trial designs. This is good news, statistical plans are always a point of contention during trial design and the submission process. This guidance lays a line in the sand and will remove some of the reviewer-to-reviewer variance present in the current FDA staff.
> This guidance lays a line in the sand and will remove some of the reviewer-to-reviewer variance present in the current FDA staff.
That would be nice, but my experience is there can be quite significant variability between reviewers in different teams/groups, even on topics you'd think were well-established for many years, and for which there is existing FDA guidance.
Accepting Bayesian methods for RCTs is great news and leading biostatisticians like Frank Harrell have been pushing for this change for many years. What I'm most interested to see: will this actually be implemented in practice, or will it be incredibly rare and niche, like Bayesian methods are currently in most biomedical fields?
Regulators are generally really conservative. Spiegelhalter et al. already wrote a fantastic textbook on Bayesian methods for trial analysis back in 2004. It is a great synthesis, and used by statisticians from other fields. I have seen it quoted in e.g. DeepMind presentations.
Bayesian methods enable using prior information and fancy adaptive trial designs, which have the potential to make drug development much cheaper. It's also easier to factor in utility functions and look at cost:benefit. But things move slowly.
They are used in some trials, but not the norm, and require rowing against the stream. This is actually a great niche for a startup. Leveraging prior knowledge to make target discovery, pre-clinical, and clinical trials more adaptive and efficient.
Journals are also conservative. But Bayesian methods are not that niche anymore. Even mainstream journals such as Nature or Nature Genetics include Bayesian-specific items in their standard submission checklists [1]. For example, they require you to indicate prior choice and MCMC parameters.
[1] https://www.nature.com/documents/nr-reporting-summary-flat.p...
Bayesian methods are incredibly canonical in most fields I’ve been involved with (cosmology is one of the most beautiful paradises for someone looking for maybe the coolest club of Bayesian applications). I’m surprised there are still holdouts, especially in fields where the stakes are so high. There are also plenty of blog articles and classroom lessons about how frequentist trial designs kill people: if you are not allowed to deviate from your experiment design but you already have enough evidence to form a strong belief about which treatment is better, is that unethical? Maybe the reality is a bit less simplistic but ive seen many instantiations of that argument around.
If choosing a Bayesian approach in a clinical trial can reduce the number of recruited subjects, I would imagine the pharma industry is strongly incentivized to adopt it.
Moreover you can manipulate your results by disingenuous prior choices, and the smaller sample you have the stronger this effect is. I am not sold on the FDA's ability to objectively and carefully review Bayesian research designs, especially given the current administration's wanton disregard for the public good.
Please watch https://www.youtube.com/shorts/2TWu_4gd-ik
Been used since the 90s.
The application of Bayesian probabilistic reasoning in general (as described in this video) is not the same thing as "Bayesian statistics" specifically, which usually to modeling and posterior inference using both a likelihood model and a prior model. It's a very different approach to statistical inference both in theory and in practice. This creator himself is either ignorant of this distinction or is trying to mislead his viewers in order to dunk on the FDA. It's obvious from the video comments that many people have indeed been misled as to what Bayesian statistics is and what the implications of its might be in the context of clinical trials.
Anything noteworthy here? I noticed they did not mention causal inference.