Rishi Bansal (Ontario & Hertford 2023) is a DPhil candidate at the Nuffield Department of Population Health with a focus on clinical trial methodology.
Tell us about your research
I'm doing my DPhil in Population Health, it's my third year now, and my thesis is about exploring and describing the methods and performance criteria required for Bayesian methods to be applied to large-scale randomized clinical trials. Essentially, we're looking at different statistical methods to apply to drug and device trials to see how well those methods perform, and how robust they are in the real world. We’re trying to offer guidance to the medical community - how do we make sure that these trials are really robust and producing good results that we can trust and then use to translate into actual clinical practice.
So you’re helping people who are designing clinical trials to design them better?
Yes, that's really what my research is - how do we design these trials to give them the best chance of being successful. To me, successful doesn't necessarily mean that the trial is positive, but just how do we give it the best chance of providing the medical community with a really definitive answer one way or another. That's what we really want to get at through doing trials - can we get a definitive answer that we can translate into clinical practice and tell people should we use this or not?
What's difficult about setting up a clinical trial at the moment?
Problem number one is that they're just so expensive. They're really, really expensive to run globally, and the price tag of a clinical trial to test a new drug has only increased in the past 10, 20 years. As everything that you're doing costs so much, you are really trying to be very careful about all the choices you're making, how many patients you to enrol, how many countries you include.
And then there are so many unknown quantities that go into the scientific and statistical elements - we don't actually know whether this drug works or not before we start a large phase 3 trial that's intended to study the efficacy of the drug. We may have some idea based on previous trials that have been done or prior knowledge about the drug or similar drugs, but we don't actually know until we've done this large-scale trial.
That in itself is a controversial statement these days - rhere's lots of debate about how do we know that something works, do we need trials to show that or not? Obviously, I'm biased given that that's what my area of research is in! But there are so many questions about the statistical quantities, the scientific quantities, the expense, and the logistics can make it really challenging. So this all comes together in a uniquely complicated mess, but that's the fun that we get to sort out.
So is it just about making trial cheaper, or more than that?
I think it is more than just making it cheaper. There are lots of things that can be done to try and reduce the expense, and in particular, Bayesian methods have been put forward as one way to make trials cheaper, because through all sorts of statistical methods, you can potentially reduce the size of your trials, and you can stop your trials a little bit earlier than you might be able to otherwise.
But my work looks into disentangling what's happening when we make those decisions - are we actually benefiting from having fewer patients in this trial or making it shorter than we would normally plan? In what scenarios is that completely reasonable and still gives us a really strong answer to the question that we set out to ask? And what are the scenarios in which it wouldn't be appropriate to stop the trial early or enrol fewer patients, even if that means it's going to be cheaper and easier to run? Might the entire medical community benefit from having a much larger, much longer running trial that's going to definitively answer our question as opposed to one that's going to give us a more uncertain answer but might be a little bit easier to do.
Can you explain Bayesian methods to a non-scientific audience?
It's actually a really intuitive thing because it's basically how we think about the world or think about anything really. Bayes' theorem, which was written down by Reverend Thomas Bayes, says that you have a prior probability or some prior information about an idea, a question, or topic; you have some data that you're going to observe from an experiment or some new information that's coming in, and that this data is going to somehow modify your prior information or belief into what's now called a posterior belief or a posterior distribution or probability.
So it's really just as simple as that: we know things about the world just from our experiences living in it or, in a medical setting, because of other drugs, other trials, other information that we might have before. Now we have a particular scientific question at hand, we're going to run an experiment, we're going to collect new data, new information. We're going to take that in, we're going to see how does that new data modify what we knew before. Does it agree with what we knew before? Is it completely discordant from what we knew before? And can we put these together, in this case mathematically, to produce a probability that represents what we now know after having done this.
Is applying that to clinical trials a new thing?
Bayesian methods applied to trials have been around for 50 plus years. There are some fantastically written papers from the '60s, '70s, '80s, where you have these statisticians debating each other in the medical journals about whether do things Bayesian or do things frequentist or more classically. This debate has been going on for a really long time.
But it's really only come to the fore in the past 15 years or so, largely because a lot of the machinery behind being able to run Bayesian models is simulation, and that requires a huge amount of computing power. Prior to 2005-2010, the computing power that was available to the average person running these trials was just not enough to be able to support that level of simulation. And the computer packages that were
necessary to run these things were not written until the late '90s and early 2000s. And then in 2003 there was the first ever FDA approval of a drug based on a Bayesian analysis. That’s really when the medical community realized that this is something that is here, that's acceptable to regulators, that's scientifically sound, and the number of trials that have been using Bayesian methods since then have really skyrocketed. Especially during the pandemic, there were a lot trials powered by Bayesian stats, and those were all published in all the top journals. So a lot of people became more familiar with the concept and since then, it's really taken off.
But in my eyes, this is still a field that's still getting its legs. There's so much to explore, there's so much that we don't know. There's so much that we haven't decided as a community on the best way to do this. The FDA only released regulations, draft guidance on applying Bayesian stats to clinical trials two months ago. These guidances sometimes can take a decade to actually reach their final form. So it's a really exciting field to work in because it's exploding in popularity and relevance. That also provides so much opportunity to look into the unexplored areas and try and leave a little bit of a mark, and guide the field in a way that we think is best.
Has COVID changed the nature of clinical trials?
COVID was a really good example what to do, what not to do. There were lots of trials that were done at the time that were really small, that did not have very many patients, that didn't run for very long, and produced results that weren't particularly helpful to anybody because the precision and the certainty around any answer was just not nearly enough to what a clinician working in the hospital, who has patients who are dying in front of them, would need to know.
On the flip side, there were a few trials, including the RECOVERY trial, which was run by my department here at Oxford, which was the complete opposite of that. They took a very different philosophy - they wanted to test all kinds of drugs that people were interested in or had a hypothesis that would work for COVID. But they would only do the trial if they could enrol thousands and thousands of patients and wait until they were certain that they had an answer of whether or not a drug worked before releasing those results out to the public. Therefore, a lot of what we found out about how to treat COVID and what drugs do and don’t work came from RECOVERY, because they took that philosophical approach that we're not going to stop running this trial until we have an answer that we feel that clinicians and governments around the world will be confident in.
So what next, after you finish the DPhil?
I'm a medic by training. I finished med school before I came here to do my PhD, and so I'll be going back to do my residency training in internal medicine for the next five to ten years to get my clinical training to go along with the fantastic research training that I've got now. And, hopefully, I will have a career as a clinical scientist to look forward to, where you get to, in a very privileged manner, split your time between seeing patients and working in the hospital, and having dedicated time to work on research.