Welcome to the Oxford University Psychology Podcast series, today I have Andrea Cypriote, who's associate professor here at Oxford University and editor of the Journal for evidence based Mental Health. Good afternoon, Andrea. Good afternoon. Thank you for coming today. Your research is about Masroor Analysis Network, Métro analysis about synthesising data to provide helpful information to to doctors. So maybe we could begin by just talking about how you got here in the first place.
How did you start thinking about these these things? Evidence synthesis is mainly what I'm dealing with in terms of my research. And I started many years ago, about 11 years ago when I was here in Oxford as a resident in psychiatry. I trained in Italy, in Verona, but I spent nine months here in Oxford. And I have to say that it was John Carlos who inspired me and told me all about evidence synthesis. So that was where it started. OK, and you began by looking at meta analysis of medications.
Is that right? Yes. I like the idea of doing something systematic and so collect to have a comprehensive use of all available evidence, but at the same time trying to synthesise all these evidence based studies into one pooled estimate. Because this is something that can really inform clinicians because you have to have one fear to work with and not a lot of different small studies. So that's the thing I like in evidence synthesis.
So methodologically rigorous, but at the same time having a bottom line message to give to clinicians. Can you give us an example of that? Well, I have in mind two example. One was the piece of research I did in 2000 and three and four, and it was published in 2005 with John and Keith Horton about suicide and lithium. So what we had was a old and quite a numerous, many trials about lithium in affective disorder, mood disorders, lithium versus placebo.
Even the most famous have been lithium versus other drugs. And we focus on the prevention of suicidality, which means suicide and deliberate self-harm in people allocated to lithium or other compounds. And what we found here was that even if lithium, the difference was not significant at the state level, when we pulled all the studies together, we found a striking effect of lithium in preventing both suicide and also the level of self-harm.
The second example is many years later, when we address the issue of antidepressant for major depression, what we wanted to have was not a simple standard metabolises A versus B, but we wanted to compare everything versus everything. And that's the so-called network with analysis and collecting data.
It was about 27000 patients, 120 studies and having a clinical clear bottom line that something was better than other drugs or other drugs were clearly worse than others was was really a good example of the powerful evidence. And you can have in this field as a psychiatrist working with these medications, I would definitely use that evidence in my practise and discussed the evidence that you've published with with patients directly. And it's been very helpful to have that data synthesis.
But if this isn't an easy thing to do, is that this isn't something that you can just take off the shelf and and do as a staff research because the methodology is very complicated. So, I mean, how is it that you've developed these these techniques that can test such a vast array of different interventions and compare them with each other? It's a minefield because it's very, very easy to make mistakes.
It started many years ago when I met Georges Allante, a statistician coming from with a strong background in mathematics, pure mathematics. And I talked to her about which was the clinical problem. I had so many antidepressants and what to do. Is there anything we can use in terms of evidence synthesis to. Grasp a very robust message, so we started developing the methodology, it was a methodology used in other fields of medicine, Nevine Psychiatry. So it was a sort of pioneering the field.
And we did it with many trials and errors. And in the end, I learnt the methodology. But what I know now is that you need a proper statistician to do this afterimage analysis. And also a statistician skilled in natural talent is not a simple statistician on.
The second thing is it's very important to be absolutely sure about the results because when you do electrometer analysis, it's a process three to four years every instrumentalise, you collect the data and the statistician runs all the analysis of the syntaxes. But many times the first, the prelaw preliminary results are wrong because so there's a it's not because as team work, you have to have the knowledge of the clinical understanding of the methods and they apply.
The statisticians are playing with numbers. And what I do is to do it blind so they don't know the names of the drugs. They know the ABC and drug Ajram B2C. So it's completely blind. But when we unblind the results, we need to talk with statisticians and clinicians of different with different expertise to understand whether it is correct or not. It sounds like you've learnt a lot about the process of developing that methodology. What would you say or what advice would you give?
You mentioned that there's there's not always good Métro analysis out there. There are some bad meta analysis how to tell the difference. I think all clinicians should be able to understand the difference. And there are a few key issues. One is, first of all, to look at the evidence, the primary evidence, primary studies, which means table one, the table of the paper should report all the studies included.
And it's a very useful starting point to understand whether that methodology is really answers the question that they report at the beginning of the paper. The second thing is how they report the data, because as long as the data in the first place are reported in a transparent way, which means replicable, we can you can you have the figures, the numbers, the denominators, and you can redo the analysis. This is a good proxy that they didn't respond way and it's in a reliable way.
The other thing is the journal. So a good journal tends to publish very good stuff, but it is not the case. So it's not necessarily the case. It's not always the case. So really what you're doing is, is providing clinicians with a series of very helpful studies that can synthesise a lot of data and sort of give give clinicians an answer. Use this antidepressant medication. Use this dose of antipsychotic, for instance. So that's really the building blocks of evidence based medicine.
And I was just wondering what you think is of evidence based medicine, more evidence based practise that's occurring in psychiatry. What do you think of it? Do you think it's this happening? Do you think psychiatrists are operating within the evidence base? Well, I think this is the best framework we have, even though it's not ideally the optimal situation. But evidence based practise, evidence based medicine is what I'd like to have as a patient.
And I want I'd like my psychiatrist and my physician to use this kind of approach because it's a combination of the clinical circumstances, circumstances, the patient's values and preferences and the best available evidence, which means not only the most robust evidence, but also the most up to date, because the results change over time, may change over time. I think there's a gap in the implementation of evidence based data into practise.
And I think psychiatry is particularly difficult because we might be psychiatrists, we might be a bit ideological. On one hand, there's a lot of debates about more ideological things rather than real clinical problems. And what I also worried is that some people have a too simplistic approach. They tend to simplify too much. So we I like the idea of having a bottom line message, but at the same time, clinical practise is so complex.
But you need to have some. Points and references, but the actual decision is between the Medick or the mental health professional and the patient, some of your recent publications have been about the doses of antipsychotics and antidepressants. Do you think that's that sort of level of detail should be left up to clinical practise and and titrating doses according to the individual patients they see?
Or should this all be regulated through guidelines? I think what medicine and psychiatry, the degree of freedom of clinician has to be preserved. Definitely what we wanted to address is a twofold question.
One is, can we give a sort of general advice about doses, say not to the press and also in terms of comparability between different depressants and different those, on the other hand, is the never ending story of the clinical trial using different doses versus placebo favouring the comparator of investigational drugs. So it was also a work we wanted to do to clarify some issues in terms of regulatory policies.
So a bit of both really, Muzaffer, clinical expertise, but we need to have the evidence base to back it up in your understanding of, you know, expert knowledge about the way the current evidence is for prescribing and in mental health, what would you say the gaps are or where do you think we need to understand more at the moment what we are trying to do?
And when I say we is a group of people, a network of people interested in this synthesis and measurement analysis, we are trying to summarise all the evidence for pharmacological but also non pharmacological interventions in the main disorders in psychiatry, unipolar depression, bipolar disorder, schizophrenia, PTSD, anxiety, panic disorder.
And the idea is then to use this data to build a sort of algorithm, because what we can do is to use the natural metabolises technique, not only with summary data from primary studies, but also with individual patient data. And we use the individual patient data. We can build these algorithms, try to find the treatment indication, according to the number of people, that the severity of illness and know country or whatever it is.
And then the big plan is to start monitoring real patients, real world settings, monitoring outcome after prescribing treatments. According to this algorithm, at the end of a follow up period could be months or years. We can use this data via a learning machine process to implement and feedback the original algorithm. And so the idea is to overcome the dichotomy between randomised data and observational data and try to merge them into a real world scenario.
So starting from randomised evidence, having treatment indications, then observational data to reinforce implement change, which is the original thing. So we you're suggesting this is actually quite radical. You're suggesting that we might begin to have evidence about what medications a patient should use if they're female, if they're middle aged, if they're planning on what race they are. So all this kind of all this kind of information is going to change clinical practise quite significantly?
Well, I think it's what we as doctors and what we what we see in our clinics almost every day. So people respond differently. People do not respond. So and everybody has different ideas. So if we can collect all this data and look at the data we have already collected, that's really informative. The potential and we have examples in neurology stroke cancer anthologies are using data of previous studies. They found incredibly powerful differences.
Try to find treatments according to the degree of to nose's to gender differences, no age or different things, I think. Yes, in psychiatry we need innovation. We need new treatment. We need to and academia is should have this mission trying to discover new treatment and better treatment for our patients at the same time, or the old treatments like lithium or other drugs we know are really effective. We need to understand why they work, but at the same time try.
To understand how we can use that better, it sounds really interesting that that that work and I look forward to hearing more results in the future, but maybe we can end by just some thoughts from you about, well, what you'd like to say to potential junior psychiatrists or medical students who might be interested in a career in academic psychiatry or career in psychiatry. What what advice or words of wisdom would you have for them?
I would encourage anyone to to do this kind of career because it's fascinating. It's exciting. And it's also very important to find someone. I was lucky to find someone who inspired me and taught me a lot of things about which is the mission of people in academia. The you have to be free thinkers, a completely open mind. At the same time, there's a dose of courage and investment in things you think are important and not necessarily what the average people say.
So to be quite innovative in your way of thinking and doing and this is from the personal point of view, from a more general point of view, I think we people who work in the academia and who try to work with the younger generation, we need to fill these gaps and we need to be better example, better models, because there's a lot of bad models and it's difficult to find someone. And it's also a matter of availability time to have the time to spend with the younger generation.
As editor of the journal Evidence based Mental Health, what I want is to be able, with a boring thing like evidence based synthesis and data synthesis, tried to show how rigorous a methodology can inspire clinical practise on an everyday basis. And what I'd like to do is engage younger generation with examples. And this is the reason why we started the Google Hangouts to treat a child's new use, the social media to engage young people because we need someone with fresh ideas and with the energy.
Interesting. Great speech Tuesday. Thank you for your time. Thanks. Thank you. Thank you for tuning in to another podcast with the Oxford University Psychology Podcast series. Goodbye.