So thank you for the invitation. And I'm very happy to be here to to present the results of this of this project.
But it's more an opportunity to discuss how, as a clinical academic,
I'm trying to bridge what is the best available evidence to something that can really form the treatment decision making process and our patients.
First of all, yes, I don't have any conflict of interest.
I've never received money from the pharmaceutical industry.
And I think it's important to say this because in psychiatry and psychopharmacology especially, there's a lot of ideology.
And I think it's important to state that I don't have any conflict of interest on this matter.
So this is a master's about evidence synthesis.
And I think it's good to start with what we consider the the best in terms of the hierarchy of evidence.
So this is what we usually teach our students.
And the idea is that depending on the specific questions in this case, for instance, about therapy, which means whether a treatment is effective,
we all know that level one is randomised controlled trials, evidence from randomised studies,
level two is cohorts, observational studies, cohort studies first and then case control.
And the idea is also that within level one, if we have a systematic review, a collection of many trials,
this is likely better than having an individual study because of the external validity, the generalisability of findings.
And also, I mean the more the merrier.
So the statistical power, if you do a mat analysis is, is much, much more informative.
And this is what standard meta analyses do.
So you have a pool of studies comparing, let's say, treatment, eh, versus placebo.
And in the end you have a pooled weighted estimate comparing a versus placebo.
If we say that on a specific topic or specific question, we also have a pool of three different studies comparing A versus B.
What standard meta analysis do is to basically be able to summarise and pool all the
evidence a versus placebo in one single estimate A versus B in one single estimate.
But the problem is that E as clinicians, basically, these cannot be really informative because we know what is one head to head comparisons.
We know what the estimate between another head to head comparisons, but we don't have the full picture.
So in a nutshell, what we do with the network match analysis is in a set of trials comparing over placebo and a set of trials comparing A versus B,
we use a treatment in common like A to calculate the indirect evidence B versus placebo.
This is the idea behind the approach so called network match analysis or multiple treatment meta analysis.
And the reason why it is informative for for for clinicians is because if we have a network of
experimental interventions which can be in this case antidepressants but can be any kind of treatment,
we can summarise all available evidence into this network and the graphical representation of the network is also, I think, important.
So in this specific situation, the size of the node is proportional to the number of patients randomised to a specific intervention for oxygen.
Here you see many patients randomised to fluoxetine, not so many to be protean and the lines represent the number of direct comparisons.
So the studies comparing one treatment versus the other and the width of the line is proportional to the number of trials comparing to treatment.
So many studies comparing fluoxetine with certainly not so many comparing escitalopram
with bupropion and actually no studies comparing reebok's acting with vitamins effect.
So if the question is which is the difference between Reebok sitting and witness have been to licensed antidepressants on the market?
I carry out a systematic review and the answer is no data.
So with this network of treatment, I can kind.
You lay out the indirect evidence for a boxer to move to a pin going by a common comparator, like, for instance, fluoxetine.
So one of the advantages of network meta analysis is to fill the gaps.
But I think that the most important advantage of network analysis is their comparison between direct and indirect evidence.
So for comparison, like certainly versus fluoxetine with many,
many trials comparing sensors in fluoxetine, we have a direct estimate, the solid red line.
But using the network I can also calculate, for instance, via venlafaxine the indirect evidence or green dotted line.
And what I can do is to compare the two and check the so-called consistency of the network.
Consistency of the network is where the direct and indirect estimates are showing the same results or similar results.
The other thing is that I can check the consistency of the estimates, certainly in fluoxetine,
not only via venlafaxine, but I can go by any closed loop within the network.
So via citalopram, by fluoxetine.
These are three not loops, but I can also use fluoxetine escitalopram paroxetine and certainly in phone notes or any kind of number of nodes.
Of course, the higher the number of nodes, the less informative is the comparison because I increase the uncertainty around the the estimate.
So in this kind of network, I think, which is very well connected.
Another important thing to consider is the so-called geometry of the network,
because that comparison between direct and indirect, the consistency of the network can be checked only if I have closed loops.
If I don't have closed loops within the network, I cannot check consistency.
So the advantages of net from its analysis,
I can compare interventions which haven't been directly compared in any trial, fill the gaps and Murtaza remarks.
And then I can use it. I can have a comprehensive use of all available data, direct and indirect, as I showed.
I can improve precision and I'll show you in a bit with the antidepressants met analysis how we can improve precision.
But basically we add the confidence intervals so they they increase the precision.
And I can also rank the treatments using different methods.
One of the methods we use to rank the treatments from the estimates of measurement analysis is this one.
So we estimate for each intervention, which is the probability to be in each position.
This is a fictional example with only four treatments just to exemplifying the process,
but basically using the estimates that we calculate from the natural naturellement analysis which can calculate for a we can sorry,
we can estimate for a which is the privilege to be the first which is 25%, it is 50% for B, it is 25% for C and zero for D.
So can I ask you the winner is exactly 50% probability the second Y.
50% to be the second. The third. Y Yeah.
And fourth, of course. D Also because you have 75%.
And that's exactly the point. So we look not only at the actual ability to be in each position, but also what we call the cumulative probability.
So the probability to be among the best three is 75%.
For a 100% would be 100% for C, only 25% for D.
And this is very important because one of the most common mistakes in natural rate analysis is to
consider only the probability to be in a specific position and for treatment with a lot of uncertainty.
You have a huge probably to be the first huge probability to be the last.
So if you look only the probability to be one position, you don't really understand which is the wrong of a treatment.
We have a great example. There was a paper published in the BMJ about pharmacological treatment of GAD, the generalised anxiety disorder,
and they use the ranking in the wrong way probabilities to be the best so called, and they rank fluoxetine as the best treatment.
And of course it was the wrong approach. We we wrote a letter to the BMJ.
They didn't want to publish our reanalysis.
And I was very happy now that a couple of weeks ago in The Lancet you find in that too much analysis about good done properly.
Okay, so it's a very common mistake. So this is a sorry for the theoretical background, but it's just to be sure we are on the same page.
So now the story about the antidepressants we started in 2009 publishing this a paper in The Lancet,
which was the first at that time we called it multiple treatments, met analysis, the first natural met analysis in psychiatry.
And at that time, I was finishing my residency in psychiatry.
And one of the key questions, I was in Italy. I'm Italian, by the way.
I'm sure you can pick up my terrible English accent.
Anyway, I was finishing my writing residency and one of the key question was for patients we used to see in our clinics with the
patient with depression was which is the antidepressant to prescribe to these patients and asking my senior colleagues,
they said, well, usually the last one, the last marketed antidepressant is the best because they are all the same in terms of efficacy,
slightly different terms of acceptability, but the newest is probably the best because they have less sexual dysfunction or they are better tolerated.
I didn't buy the argument. I still was in favour of an evidence based approach.
So what we did was to carry out this systematic review, which is actually a systematic review of 12 systematic reviews.
And the clinical question was very clear to me. So I have a patient in front of me.
The patient is depressed and the patient has agreed to start a pharmacological treatment.
Okay. So which is, if any, the best treatment for this patient?
So for this reason, what we looked at was only second generation antidepressants because according to the guidelines,
we should prescribe a second generation antidepressant. We did not include placebo because the question was not whether to the presence work.
The question was, which is because we already decided to prescribe an antidepressant.
And of course, because the patient was depressed, the outcome was acute efficacy and acceptability of this treatment.
So the project was called Mangi.
We included 117 randomised controlled trial, 26,000 participants, about 12 drugs.
And I think names are important.
Names are important because especially in network meta analysis, you are working with a multidisciplinary team and there's a lot of email exchange.
So it's good to have a title like Manga Meta Analysis of New Generation Antidepressants.
As I said, we included only active drugs, no placebo, no all the comparator,
because according to guidelines, second generation antidepressants should be preferred.
And it was the first natural meta analysis.
So we decided to keep it simple, but we wanted to look at two primary outcomes which were clinical informative.
One was response. So after eight weeks of treatments, how many patients respond?
50% reduction on a rating scale, which is pretty standard measure second was at eight weeks.
How many patients are still on treatment? So called acceptability dropout rate?
Of course, we had a lot of there was a lot of controversy around this met analysis because for the first time we proved that there.
Differences between antidepressants. But a lot of criticism was about the exclusion of all comparators, the exclusion of placebo,
because there's a lot of debate in the literature whether antidepressants really work.
So after a few years, actually, we started in 2011, 2012, and we published a paper six years after in 2018.
So it took a long, long time to do Greece out of that group of research and investigating specific efficacy of individual drugs for acute depression.
It's it's a long title, but it's a very nice story about Griselda.
I don't know if you're familiar. She's a fantastic woman that basically when I started the project, I knew it was something very long.
And we basically what we needed word was a lot of determination,
endurance and will to fight against all the difficult things we may have encountered or during and during the process.
And Griselda is a lady that was basically it's a difficult story today because she had everything she should not have had.
Like she was treated badly by the husband. And basically she was a slave of her husband, treated poorly and neglected, and in the end,
the husband to punish her for nothing because she didn't do anything wrong.
She he pretended that he was marrying their daughter and she was asked to be the witness of the marriage that something terrible.
But in the end, all these was a sort of test of the husband to test how good and how strong she was.
So terrible story. Nice, happy ending.
But the very reason why I like the story of Griselda is not only because it's true that women are much better than men,
but also because if you ask when I present this story to British people, they know, they say, well, I know Griselda, it's chose her.
It's a novel, a story narrated by Chaucer in his Canterbury Tales.
But that's where British people are wrong because it's Italian.
So Tozer decided to steal the story from Boccaccio.
And in this specific period of Brexit, I'm so proud to keep saying that these are actually Italy and not UK anyway.
It's a personal revenge because last night I was very, very worried about the Parliament.
Okay, so for Griselda, we had to rethink the project.
And the first question was, do antidepressants work for depression?
So are they an effective treatment for depression?
First question if yes, which is if any of the best treatment.
So this is why in grizzle that we have placebo, we included also not only the two previous outcomes, but we also included continuous outcomes.
We wanted to expand the number of drugs.
So not only the newer or the newest antidepressants on the market, but also all the comparators.
And to be methodologically sound, we decided to include antidepressants which are in WTO essential list of medicine to be informative globally.
Of course, we were very careful about the search strategy, including publish and publish and also all the regulatory stuff about drugs.
And in the end, we have 21 active treatments and placebo, 5522 double blind randomised controlled trials, less than 120,000 participants.
Just to give you an idea, this is the largest net promoter analysis in medicine.
So as I said, the rationale is long lasting debate about efficacy of antidepressants.
Are there really differences between these individual drugs?
So we focussed again on the acute treatment of adults with unipolar, major depressive disorder.
As you know, we need to have a specific question to answer.
We have parallel projects about long term effects or treatment resistant depression.
But this paper is only about the acute treatment of adults with unipolar depressive disorder.
The reason why we, especially for not too much analysis we need to focus is because of an assumption we do with measurement analysis,
which is called transitivity.
So when we have the network and this is a good example, if we have a network of treatment to be able to do a network meta analysis,
the assumption of transitivity is crucial because basically we assume that each patient included.
In each of the treatment could be theory randomised to any of the other treatments.
So that's the transitivity which allows us to use the indirect comparison to compare two interventions which haven't been compared.
And the problem, I mean, how we violate transitivity is also important not only in terms of how we deliver the treatment.
For instance, we include placebo,
but we cannot have placebo as an injection compared to placebo as a tablet because especially for depression or pain,
the way you administer placebo can have an effect on the outcome.
And the other important thing, especially in the field of antidepressants, is the effect of the drugs should be stable over time.
Because if you compare and all these are all new drugs, but for all comparisons like amitriptyline,
if you compare a drug with placebo and in the seventies the response rates to placebo was 20%
and you compare escitalopram with placebo in 2000 and the response rate of placebo is 40%.
This is a violation of transitivity because you lumped together placebo assuming that we have the same effect modifiers.
But this is different.
So the first thing we had to do was to be sure that we were comparing similar with similar and that the transitivity assumption would be confirmed.
So we go back to these in a moment.
So all lies and second generation antidepressants, not only in Europe, for instance, in Japan, they don't have a vaccine, but they have been nasty.
So this is why I mean, aspirin is in the list. We have full reference, first generation antidepressants and placebo.
This is the paper which is open access, which means that you have to pay a lot to the Lancet $5,000.
It's amazing. But it's open access, also the data set.
So all the data we analysed, six years of work are freely available on the website.
In order to incentivise people to replicate the analysis and find any mistakes we may have made.
So as I said, only double blind randomised controlled trials published in unpublished antidepressants
as monotherapy oral adults with a primary diagnosis of MDD major depressive disorder.
We use standardised criteria for instance.
For this reason we had to exclude a lot of Chinese studies and we also consider excluded people who had depression with cancer or are like,
ah, rheumatoid arthritis of these kind of medical comorbidities.
The search is up to January 2016 because it took us about a year and a half to analyse the data and find the right approach.
And we searched manually all the international trial registries, the websites of regulatory agencies, and also we contacted all the study authors.
So this is why it took us a lot of time. This is the list of 21 drugs up to here is the list of new generation
antidepressants from my go into 40 sitting in a sabbatical order and these are the
for reference drug I tripling compare mean among the TCAS trazodone and the
incident because they are antidepressants with a specific side effect profile.
For instance, trazodone is often used by GP's because of the A side effect profile in terms of insomnia.
As I said, we included bipolar psychotic disorder, depression, treatment resistant depression,
serious mental illness and this is the list of outcomes, the two historical one.
Just to be sure, we could compare the results of 2018 with 20 or nine because things as we know can change in evidence based medicine.
But we added continuous outcome change on symptoms, we added remission to response.
And also not only dropouts, all college dropouts, but also dropouts due to adverse events.
Because these is a review to inform clinical practice, we included data only within the therapeutic range of the antidepressants.
We excluded low dose or too high dose in terms of statistical analysis.
Of course, we use alterations in the dose mean differences because we have both continuous and dichotomous outcomes.
We look at the heterogeneity. Of course the transitivity assumption was evaluated looking at the distribution of the
clinical and methodological variables that could affect could mean effect modifiers.
But transitivity we cannot test transitivity with a statistical method.
So this is why all the discussion is mainly about the clinical issues that may have an effect as effect modifier.
Why consistency? We have quite robust methods to check consistency and we use both the local method and the global method.
I'm very proud of this because in the review team I wanted three statisticians to analyse the data independently using three different softwares,
just to be sure that the results were consistent, consistent independently of the way or the statistical software you analyse them.
And of course for the certainty of evidence we use the standard which is now agreed.
We took the opportunity also to carry out subgroup and sensitivity analysis because for instance, we know that study can be an effect.
Modifier sponsorship is a big issue in psychiatry, especially in antidepressants, literature, severity,
baseline, the dosing schedule and also the small side effect, which is somehow a proxy of publication bias.
And the novelty effect, because the same drug, when is the experimental drug,
tend to be more effective than when the same drug is the comparator, which is a strange thing, which is not justified by the biology.
So we look also for the novelty effect and in terms of sensitivity analysis,
because we imputed response rate from the continuous data, we excluded studies which did not report a response rate.
Of course we excluded study that did not use all accepted those in all arms.
So not only the arms but all the study, the unpublished data.
Yes. Because we look for unpublished data.
Systematic. So if the same study reported, published and unpublished data, we gave preference to the unpublished data assuming they are more reliable.
So we included only studies which provided also unpublished data and and so on.
So this is the, the flow chart.
We started from around 30,000 references.
And from this side is the electronic data sets we had.
We included about 420 studies from the electronic datasets databases.
But we found 86 unpublished studies and also by a personal communication or hand searching of reference list,
additional 15 randomised controlled trials.
So in the end, the 522 are in the large majority from electronic databases, but a significant proportion of the data is unpublished as expected.
This information, I think, is also important. We reported the number of studies per drug compared to placebo or another active comparator.
And of course, drugs like fluoxetine or paroxetine, they have more than 110 trials.
Most recent drug like does similar vaccine or level in nasty.
They have only less than ten studies and that's expected.
However, the the bottom line message is that the amount of evidence is different across different drugs.
These are the two networks network network plots for efficacy and acceptability.
Can I ask you what you think about this?
I told you that the shape of the network is very important to to gather some information, to guide the interpretation of the results.
So what do you think about this? To.
So the colours are different. I know, but. Exactly.
That's. That's one of the important things. So the shape of the network is basically identical.
The only difference, if you can see, is that between Bupropion and Tripoli, we don't have studies reporting efficacy.
We have one studies reporting data about acceptability.
But apart from these and another couple of examples, the same studies reported data about response and dropouts,
which is good for us because the initial assumption was the primary outcome is a combination of response and dropouts,
because we want to know whether after eight weeks people who responded and whether they are still on treatment,
which is a proxy of acceptability and tolerability as well.
So the shape of the network is almost identical.
And the other thing which is important from a methodological point of view, is that in the end the network is very well connected.
So we have a lot of head to head comparisons.
I don't know how familiar you are with the literature in psychiatry, but the great majority of trials are placebo controlled.
We did a natural meta analysis about acute mania published in The Lancet a few years ago,
and the shape of the network is called Star Shape Network because you have placebo in the middle.
A lot of comparison with placebo, but only if you had to have this like like a star.
So this is a very well-connected network, which means for us we can check inconsistency.
So we have more, more. The results are much more robust.
Sorry. So let's ask the first question.
So whether antidepressants work. So the comparison here is drug versus placebo.
I use the standard forest plots, but these are network meta analysis data.
Okay. So including both direct and indirect and on the right favours drug on the left favours placebo.
As you can see here, all the drugs are statistically significantly better than placebo.
Everything is on the right.
And also there are some differences between, for instance, the top one amitriptyline and the least effective Rybak sitting.
This is a material difference because if you look at the confidence interval, they do not overlap.
So in the end, we have completely different treatments in terms of efficacy versus placebo,
but all antidepressants are statistically significantly better than placebo.
In terms of efficacy, in terms of acceptability,
the great majority of drugs are not statistically significantly different in terms of dropout rate or cause dropouts.
A couple like accumulating and fluoxetine statistically significantly better compare mean significant,
statistically significantly worse in terms of acceptability than placebo.
But if you look at the confidence interval is very close to that line of no difference.
So the figure of the paper is combining efficacy and acceptability.
Of course, in order to make it visually like this, I had to swap.
So you can see here that the confidence interval and the estimates are the other way around.
So it's a bit complicated. So this is 1.5 to 2.5.
These is the other way around. But because my paper was for clinicians, you give the message on the right favours drug on the left favours placebo.
Okay. The big question is whether these differences translate into something which is clinically meaningful.
But this is the second part of the presentation. So the remarks that this is the least effective.
Can we try to combine the two outcomes in one graph?
So this is what we did. So we have efficacy here.
It's the same data, same results. So efficacy on the x axis one using the references, Reebok City, these are all the other data and this is placebo.
So in terms of efficacy, so this is the line of no difference.
Antidepressants are better than placebo and this is acceptability.
This is the line of the difference. You can see that the great majority of antidepressants are similar to placebo.
A few are less acceptable. But this is the graph.
If we use all studies, what happens if we exclude placebo controlled trials?
If you exclude placebo controlled trials, keep these as a reference.
Look at what happens. So this is with placebo.
If we exclude all placebo controlled trials. So we have only had two trials.
This is the figure. So and the box it in is in the same place is always the reference.
So basically if you include placebo in the network, all the differences between antidepressants shrink.
So the problem is to interpret this. Of course, we reported the figure of the paper is this one.
So we had all studies head to head. But because we want we need to interpret the data from a clinical point of view, which is the truth, of course.
I don't know. I'm just trying to suggest some interpretation.
So what we did was to analyse the question specifically,
and the paper is now published in the Journal of Clinical Epidemiology, so International Journal of Epidemiology.
So we look at the impact of placebo arms on the outcomes of antidepressant trials,
and the outcomes are efficacy, of course, dropout and dropouts due to adverse events.
So what we did was to, as an outcome, look at the probability of being allocated to placebo, the pie as dichotomous yes or no.
Try continuous 0% is head to head.
50% is two on placebo controlled trial.
Anything in between these multi arm placebo controlled trial or as a continuous variable?
Of course, because we had to have the same drug tested in placebo and in head to head alone,
the number of studies is less than the original number of 522 and so on.
We in the end included 700 and something arms.
So these is for response and these is the overall results.
So the light blue is placebo controlled trial to are placebo controlled trial.
The very dark blue, almost black, let's say is head to head trials and in the middle is multi arm placebo controlled.
Okay. So if you look at the whole can you touch this or.
Yeah, okay. So if you look at the overall results being in a two arm placebo controlled trials of drug versus
placebo response rate is less than being in a head to head trial without placebo first finding.
But if you look at the specific individual drugs,
this pattern is quite typical for many of these drugs escitalopram citalopram for toxicity and blah blah blah because of pain.
But it's completely different for drugs like amitriptyline.
So in amitriptyline trial, being in a placebo control, I mean typically versus placebo trial,
the response rate is much higher than being in amitriptyline versus an active drug with our placebo.
So the opposite. So they are ordered.
I think from here to here is the order with the block.
So the probability of receiving so the head to head trials and these is the all overall it's not a sabbatical order.
If you look at the all cause dropouts, you can see that being in a placebo controlled trial to arm,
you have a higher probability of dropping out than being in head to head.
And again, there are differences between individual drugs.
If you look at dropouts due to side effects, there's no difference.
So in the end, I think these is the most important figure of the paper.
If you plot, again, efficacy Y and dropout in the X axis and the black dots are the head to head trials and
the blue dots are the placebo controlled trials for the same drug for the same drug.
Being in a head to head trial, you have a higher probability to respond,
less probability to dropout than being in a placebo controlled trial for the same drug.
So it's not about the drug is about the study design. So in the paper, we suggest possible explanations.
And these are the two extremes. One is, of course, in, plus to and placebo controlled trial versus head to head.
We can measure the response to the drug, but of course, we cannot measure the context to our response,
what we what we call the placebo response, because we cannot measure this in a head to head trial because there is no placebo.
Okay. So the only way to understand whether the difference between the two is related to this placebo effect or
sort of contextual response would be to cheat two patients would be to grant a placebo controlled trial,
which is actually a head to head and vice versa, which is not ethical.
Well, I thought it was not ethical until I went to Australia to present this data to a
group of colleagues who are working on pain and they are doing this kind of trials.
So they treat two patients. They say this is a placebo controlled trial.
So ethically I think it's more difficult because, you know, with depression we have the issues about suicide and or self-harming.
So it's not an easy thing to do. But I think this is the only way to methodologically sound answer the clinical question.
So we are working on this. But there's another thing about antidepressant trials.
I don't know how familiar you are with the literature, but if this is the typical trajectory in a trial,
so this is the change in symptoms over time, over eight weeks,
you start with the same severity and after eight weeks people randomised to
placebo respond less than people randomised to the highest dose of the drug.
This is the lower dose of the drug and this is the active comparator.
Okay, this is a typical situation. But because they a sorry.
And at the end the difference between placebo and the drug is five points on the madras
which is a standardised rating scale to measure depressive symptoms because there's placebo.
And we know that people in placebo controlled trials tend to drop out early.
What happens is that they drop out, let's say, after one week.
And in order not to miss these information, what researchers and trialists do is to take the last observation,
and they carry forward this until the end of the study because as I said,
in placebo controlled trials also people randomised to the drug drop out early so they carry
out the last observation and in the end the difference they measure is one instead of five.
Do you know how many? Oh, I know in psychiatry we are far behind other fields of medicine because I know that very,
very I mean, more sophisticated measure methods to basically tackle the issue of missing data.
But do you know how many studies in the sample of Griselda?
So out of 522, use the LCF method to impute four missing data.
95 So the thing is, these may be something which bias the results.
So in the end, all this is why the league table,
which reports response rate and dropout rate in one table is based on the head to head and not on the placebo controlled trial.
So these are the drugs in alphabetical order,
and the idea is to be able to compare the column defining versus zero defining and be able to estimate the head to head.
So if we have fluoxetine versus circulating certainly is better than fluoxetine.
Escitalopram is better than a box, sitting escitalopram is worse than citalopram and so on.
So there are highlighted and bold, statistically significant results and for efficacy and the same for acceptability.
I will move ahead. We can go back to the results if you want limitations.
There are many limitations of the study. Of course, we may have missed studies, but there's no evidence of publication bias.
I didn't mention the quality according to grade was moderate to low or very low.
So there's a big debate in terms of how good are these results.
But at the same time, this is the best evidence we have. We didn't do any cost effectiveness analysis because we just wanted to inform the globe.
So in order to do the cost effectiveness analysis, you need to use data in individual countries.
This is an ongoing project.
Of course, we may have missed some potentially important effect modifiers and of course the number of outcomes reported here is limited.
We are now finalising the manuscript about adverse events,
but it took us about another year to develop the methodology to analyse the adverse events in the natural matter analysis.
But I think this is the main problem and this is where the controversy was in the end.
These are data which are aggregate. So it's not we're still not able to personalise treatment in depression,
even though it's much better to have a league table like this, we are still talking about average.
So this is why we need to move a step forward.
And can we use natural methodologies to personalise twittering depression?
Of course, I think the answer is yes. This is a paper we published a few months ago in psychotherapy and psychosomatic.
And this is a sort of proof of concept whether we can predict treatment outcome using,
I mean, clinical baseline and demographic characteristics of patients.
So for this specific project, which is about a depression,
we used an individual patient data network meta analysis which investigated the use of
antidepressants alone versus psychotherapy alone versus the combination in chronic depression.
We we decided to choose the example because we did a systematic review.
We had only four studies and we managed to have the individual data of all the studies.
And because it's four studies with three nodes, we have a good representation of the evidence to test the method.
But basically what we did was depending on the demographic characteristic of these patients.
So being female with severe depression, severe anxiety, very young, 31,
no family history of depression, short duration of symptoms, the trajectory of symptoms,
how the symptoms change over 12 weeks for treatment A, B, and C,
it's clear because this is a change, that C is the best option for Helen, but for a men.
So being men, nothing less, severe depression, severe anxiety, slightly old family history of depression, a longer duration of symptoms.
Actually, option B is the best treatment. So with the individual patient data, we can show the patients, which is the trajectory.
How fast is the outcome plus the outcome to to to to to B and also which is the best treatment.
But these is only part of the project.
The other big important issue is to incorporate patient preferences because we have a lot of adverse events and we know that some people can be.
I mean, the burden of weight gain can be more for me or sexual dysfunction or headache or tremor.
So the idea is to incorporate these in a treatment algorithm, and the treatment choice can inform really the ranking of treatment.
So this is that how can I say the template and how the screen of the algorithm.
So I mentioned Nick and this is his situation.
But if the preference of Nick is these are sliding box so he doesn't want sedation, doesn't want weight gain and he's fine in terms of cardiac rate.
So the cutesy prolongation is not an issue for him. This is the ranking of the treatment.
But for many made with the same characteristics, same age, same severity of depression, anxiety, same family history, but with different preferences.
So he doesn't want nausea, he doesn't want tremor, and he has a problem intimately discipline.
The ranking is completely different. So this is where we are.
We should go. And this is my Nature Reserve Professorship project.
Be able to analyse individual patient data from the trials they showed combined with the
observational data from real world patients and incorporate patient preferences into this algorithm.
You are familiar with a master's, but I'm editor of these journal Evidence based Mental Health and we published this paper, of course, open access.
So you can find all the things I mentioned about natural analysis there.
And this is the promotional bit.
We have this course in Oxford because I strongly believe that clinicians and clinical researchers should be aware of natural meta analysis,
but especially critically appraise this kind of very trendy article in the scientific literature, but often very biased and very rubbish.
Thank you.