Amphetamine, Cocaine and DAT

The brain is a tightly regulated system. Levels of neurotransmitters, for example, are regulated by reuptake proteins, which move transmitters from outside the cell to inside, where they are inactive. This means that after cells release a neurotransmitter, such as dopamine, it is rapidly taken back up again.

Interestingly, however, the levels of the reuptake protiens themselves are variable and can change in response to various things. If dopamine levels rise, for example, nearby cells rapidly increase the number of dopamine transporters (DAT), thus helping to reduce dopamine levels again. This happens when DAT proteins waiting dormant within nerve cells are sent to the surface (the cell membrane) in response to increased dopamine levels.

This much is fairly well known, but a lovely experiment from a University of Michigan team has revealed just how fast the process is. (Dopamine and Amphetamine Rapidly Increase Dopamine Transporter Trafficking to the Surface: Live-Cell Imaging Using Total Internal Reflection Fluorescence Microscopy).

The authors used a form of light microscopy which allows the membrane of a single cell to be imaged. They created cells genetically engineered to have dopamine transporter protein (DAT) which glows, because it was linked to Green Fluorescent Protein. This allowed them to view changes in the level of DAT on the surface of the cells, in real time, in living cells.

They found that adding dopamine caused DAT levels to rise astonishly fast - within just a few seconds. Amphetamine, a drug which acts on the DAT, had the same effect. However, cocaine, a drug which blocks DAT, prevented this effect.They've even made a video so that you can see the dopamine transporters bubbling up on the surface of a single cell. Watch it (if you have academic access) - it beats 99% of YouTube.

This is a fascinating result, and it underlines the fact that nothing in the brain is ever straightforward. For example, most people will tell you that amphetamine and cocaine both have stimulant effects by "increasing dopamine levels" - cocaine by blocking dopamine reuptake and amphetamine by causing the dopamine transporter to actually go into reverse and start releasing dopamine. But this result suggests that amphetamine also increases membrane dopamine transporter levels. That could have any number of indirect effects. Then again over longer time-scales (minutes), amphetamine reduces the DAT levels. That could have indirect effects too...

It's also worth bearing in mind that although this experiment involved the dopamine transpoter, other reuptake proteins like the serotonin transporter might well be regulated in the same way, which could have big implications for antidepressant action...

Furman, C., Chen, R., Guptaroy, B., Zhang, M., Holz, R., & Gnegy, M. (2009). Dopamine and Amphetamine Rapidly Increase Dopamine Transporter Trafficking to the Surface: Live-Cell Imaging Using Total Internal Reflection Fluorescence Microscopy Journal of Neuroscience, 29 (10), 3328-3336 DOI: 10.1523/JNEUROSCI.5386-08.2009

Amphetamine, Cocaine and DAT

The brain is a tightly regulated system. Levels of neurotransmitters, for example, are regulated by reuptake proteins, which move transmitters from outside the cell to inside, where they are inactive. This means that after cells release a neurotransmitter, such as dopamine, it is rapidly taken back up again.

Interestingly, however, the levels of the reuptake protiens themselves are variable and can change in response to various things. If dopamine levels rise, for example, nearby cells rapidly increase the number of dopamine transporters (DAT), thus helping to reduce dopamine levels again. This happens when DAT proteins waiting dormant within nerve cells are sent to the surface (the cell membrane) in response to increased dopamine levels.

This much is fairly well known, but a lovely experiment from a University of Michigan team has revealed just how fast the process is. (Dopamine and Amphetamine Rapidly Increase Dopamine Transporter Trafficking to the Surface: Live-Cell Imaging Using Total Internal Reflection Fluorescence Microscopy).

The authors used a form of light microscopy which allows the membrane of a single cell to be imaged. They created cells genetically engineered to have dopamine transporter protein (DAT) which glows, because it was linked to Green Fluorescent Protein. This allowed them to view changes in the level of DAT on the surface of the cells, in real time, in living cells.

They found that adding dopamine caused DAT levels to rise astonishly fast - within just a few seconds. Amphetamine, a drug which acts on the DAT, had the same effect. However, cocaine, a drug which blocks DAT, prevented this effect.They've even made a video so that you can see the dopamine transporters bubbling up on the surface of a single cell. Watch it (if you have academic access) - it beats 99% of YouTube.

This is a fascinating result, and it underlines the fact that nothing in the brain is ever straightforward. For example, most people will tell you that amphetamine and cocaine both have stimulant effects by "increasing dopamine levels" - cocaine by blocking dopamine reuptake and amphetamine by causing the dopamine transporter to actually go into reverse and start releasing dopamine. But this result suggests that amphetamine also increases membrane dopamine transporter levels. That could have any number of indirect effects. Then again over longer time-scales (minutes), amphetamine reduces the DAT levels. That could have indirect effects too...

It's also worth bearing in mind that although this experiment involved the dopamine transpoter, other reuptake proteins like the serotonin transporter might well be regulated in the same way, which could have big implications for antidepressant action...

Furman, C., Chen, R., Guptaroy, B., Zhang, M., Holz, R., & Gnegy, M. (2009). Dopamine and Amphetamine Rapidly Increase Dopamine Transporter Trafficking to the Surface: Live-Cell Imaging Using Total Internal Reflection Fluorescence Microscopy Journal of Neuroscience, 29 (10), 3328-3336 DOI: 10.1523/JNEUROSCI.5386-08.2009

In Defense of Susan Greenfield

Baroness Susan Greenfield has been taking a lot of flak these past few days for her comments about Facebook and computers in general:

If the young brain is exposed from the outset to a world of fast action and reaction, of instant new screen images flashing up with the press of a key, such rapid interchange might accustom the brain to operate over such timescales. Perhaps when in the real world such responses are not immediately forthcoming, we will see such behaviours and call them attention-deficit disorder...

and

I often wonder whether real conversation in real time may eventually give way to these sanitised and easier screen dialogues, in much the same way as killing, skinning and butchering an animal to eat has been replaced by the convenience of packages of meat on the supermarket shelf

She's taken a lot of flak, and she fully deserves it. Her comments were ill-judged and they bring her position as head of the Royal Institution into disrepute. Her speculations about clinical diagnoses such as ADHD and autism were especially dubious.

Greenfield's statements also display the vacuous obsession with "The Brain" so common today - if she'd simply said that spending hours on the internet might plausibly make kids grow up anti-social, that would be fair enough, but she had to bring the brain into it (several times in her various comments). Hence the headlines to the effect that Facebook could change or damage the brain. Well, Facebook does change the brain - as does everything else - because every experience we have has an influence somewhere in the brain. I'm reminded of Vicky Tuck on boy's and girl's brains; Tuck, however, is not a neuroscientist. Greenfield should know better.

But despite all this, Baroness Greenfield does make an important point.

At the moment I think we're sleepwalking into these technologies and assuming that everything will shake down just fine

These are very wise words. As a society, we are in danger of "sleepwalking" into social and cultural changes which we may end up regretting. Profound changes in the way people live rarely happen overnight, and they are rarely presented to us as a choice that we can either accept or reject. Societies just change, over a span of decades, often without anyone noticing what is happening until the change has happened.

One of my favorite books is Bowling Alone by the sociologist Robert D. Putnam. Putnam assembled data from a wide range of sources to support his theory that a profound change took place in America over the years from about 1960 to 1990; namely, that Americans stopped participating in community life. Union membership, Church attendance, charitable giving, league bowling, voter turnout, cards-playing, and many other such statistics fell markedly over this period, after a high peak in the 1950s. Meanwhile, solitary or small-group activities such as TV watching, spectator sports, and so on, exploded. Over the span of 20 years or so, Americans lost interest in "the community" as a whole and turned to themselves and their immediate circle of friends and family. He also makes a convincing case that this is, in many ways, a bad thing.

I doubt that Putnam's thesis is water-tight; for all I know he may have cherry-picked those statistics that support his theory and ignored those that don't. It wouldn't be the first time that someone has done that. Yet what's interesting about Bowling Alone is that even if Putnam's theory is only part of the truth, it's hard to deny that there's something in it - but it still took a book published in 2000 to bring it to people's attention. Putnam was writing about profound changes that every American will have felt to some degree. Yet these changes went un-noticed, or at least, few noticed that the various individual changes were part of a larger trend.

Putnam proposes various causes for the fragmentation of American community life, ranging from suburbanization to the increasing time pressures of work to that old favorite "the breakdown of the family". None of these were deliberate choices. Over 20 years or so America sleepwalked into a different way of life. This is hard to deny even, if you don't accept everything Putnam says. Baroness Greenfield, clearly, is no Robert Putnam. But her point about the dangers of sleepwalking is a sound one. Sleepwalking happens. It would be a pity if that message were to be lost in all the nonsense about Facebook and the brain.

[BPSDB]

In Defense of Susan Greenfield

Baroness Susan Greenfield has been taking a lot of flak these past few days for her comments about Facebook and computers in general:

If the young brain is exposed from the outset to a world of fast action and reaction, of instant new screen images flashing up with the press of a key, such rapid interchange might accustom the brain to operate over such timescales. Perhaps when in the real world such responses are not immediately forthcoming, we will see such behaviours and call them attention-deficit disorder...

and

I often wonder whether real conversation in real time may eventually give way to these sanitised and easier screen dialogues, in much the same way as killing, skinning and butchering an animal to eat has been replaced by the convenience of packages of meat on the supermarket shelf

She's taken a lot of flak, and she fully deserves it. Her comments were ill-judged and they bring her position as head of the Royal Institution into disrepute. Her speculations about clinical diagnoses such as ADHD and autism were especially dubious.

Greenfield's statements also display the vacuous obsession with "The Brain" so common today - if she'd simply said that spending hours on the internet might plausibly make kids grow up anti-social, that would be fair enough, but she had to bring the brain into it (several times in her various comments). Hence the headlines to the effect that Facebook could change or damage the brain. Well, Facebook does change the brain - as does everything else - because every experience we have has an influence somewhere in the brain. I'm reminded of Vicky Tuck on boy's and girl's brains; Tuck, however, is not a neuroscientist. Greenfield should know better.

But despite all this, Baroness Greenfield does make an important point.

At the moment I think we're sleepwalking into these technologies and assuming that everything will shake down just fine

These are very wise words. As a society, we are in danger of "sleepwalking" into social and cultural changes which we may end up regretting. Profound changes in the way people live rarely happen overnight, and they are rarely presented to us as a choice that we can either accept or reject. Societies just change, over a span of decades, often without anyone noticing what is happening until the change has happened.

One of my favorite books is Bowling Alone by the sociologist Robert D. Putnam. Putnam assembled data from a wide range of sources to support his theory that a profound change took place in America over the years from about 1960 to 1990; namely, that Americans stopped participating in community life. Union membership, Church attendance, charitable giving, league bowling, voter turnout, cards-playing, and many other such statistics fell markedly over this period, after a high peak in the 1950s. Meanwhile, solitary or small-group activities such as TV watching, spectator sports, and so on, exploded. Over the span of 20 years or so, Americans lost interest in "the community" as a whole and turned to themselves and their immediate circle of friends and family. He also makes a convincing case that this is, in many ways, a bad thing.

I doubt that Putnam's thesis is water-tight; for all I know he may have cherry-picked those statistics that support his theory and ignored those that don't. It wouldn't be the first time that someone has done that. Yet what's interesting about Bowling Alone is that even if Putnam's theory is only part of the truth, it's hard to deny that there's something in it - but it still took a book published in 2000 to bring it to people's attention. Putnam was writing about profound changes that every American will have felt to some degree. Yet these changes went un-noticed, or at least, few noticed that the various individual changes were part of a larger trend.

Putnam proposes various causes for the fragmentation of American community life, ranging from suburbanization to the increasing time pressures of work to that old favorite "the breakdown of the family". None of these were deliberate choices. Over 20 years or so America sleepwalked into a different way of life. This is hard to deny even, if you don't accept everything Putnam says. Baroness Greenfield, clearly, is no Robert Putnam. But her point about the dangers of sleepwalking is a sound one. Sleepwalking happens. It would be a pity if that message were to be lost in all the nonsense about Facebook and the brain.

[BPSDB]

Antidepressants, Placebos and the Failure of Psychiatry

Update 06 05 2009: Time readers may find this other post interesting!

Antidepressants are some of the most-prescribed drugs in the world. Yet they are also amongst the least well understood. We know little about how effective antidepressants are in the people who take them. Some antidepressants may work fantastically for most people. On the other hand some of them, perhaps all of them, may be useless or even worse. The truth is unclear.This is a minority view. Opinions about antidepressants are polarized - most people either firmly believe that they do work, or firmly believe that they don't. Yet neither of these positions seems to me to be supported by the evidence available. I don't think that anyone ought to firmly believe anything about these drugs - except that better research is urgently needed.

Another placebo meta-analysis

The issue is not a lack of studies. After fifty years of research, and untold millions of research dollars, there are hundreds of published clinical trials of antidepressants. It's when you try to make sense of the results of this great mass of trials that the problems become apparent. The latest attempt to do that is a paper from a German-American collaboration, Rief et. al.'s Meta-analysis of the placebo response in antidepressant trials. The authors set out to

Determine overall effect sizes of placebo and drug effects in antidepressant trials

In other words, they wanted to find out how much people improve when given antidepressants, and how much of that improvement is due to the placebo effect. They had plenty of data to work with. Even after discarding hundreds of trials for being too small or otherwise unsuitable:

The final sample consisted of 96 trials that reported sufficient data to compute effect sizes. The placebo groups of these studies comprised 9566 people. Approximately half of the studies were published after 1996, 68% were conducted in the United States, and the mean sample size was 86 participants.

And this is what they found after crunching the numbers:

The overall effect size [Cohen's d] of the placebo effect was 1.69 (95% CI=1.54–1.85), as compared to d=2.50 (95% CI=2.30–2.69) in the drug group. The ratio of the effect sizes suggests that 67.6% of the improvements in the drug group were attributable to the placebo effect [i.e. because 1.69 is 67.6% of 2.50].

That seems like a nice, neat and tidy result. When you give depressed people antidepressants, they get loads better (a standardized effect size of 2.50 is enormous), but most of that enormous improvement is due to the "placebo effect". However, the truth is not quite so neat.

It's a Little Bit More Complicated Than That

1. First off, none of the studies included in this analysis measured the placebo effect. The "placebo effect" is supposed to be the power of treatments to make people get better purely through making them expect to get better. It's certainly plausible that there could be big placebo effects in depression. There is plenty of anecdotal evidence that it happens.

In these studies, patients took either antidepressant pills or sugar pills. The patients given sugar pills were assessed as having got a lot better, on average. Is that evidence for the placebo effect? No, because as I've explained before, the improvement reported in the placebo group could be huge even if there were no "placebo effect" at all. The patients might have just got better spontaneously, because people who are depressed do tend to get better with time. It might have been that old chesnut, regression to the mean. Or maybe the patients only seemed to get better on average because the ones who didn't get better dropped out of the trial.

According to a meta-analysis of trials which actually did examine the placebo effect - by comparing people given placebos to people who got no treatment at all - the placebo effect in depression is at best small (Hrobjartsson & Gøtzsche 2004). However, the authors of this paper are well known for being very skeptical of placebos, and the number and quality of the trials was very low. There were 7 trials with a total of 258 patients. That's it. The only reasonable view is that we just don't know how powerful placebos are in depression.

2. Rief et. al. found that the size of the effects of antidepressants and placebos was much bigger when using "observer-rating" to measure the severity of depression, as compared to when patients rated their own symptoms. The difference between the two types of rating scale was enormous, dwarfing the drug vs. placebo difference:

In the placebo groups, there was a substantial difference between effect sizes for improvements rated by observers (d=1.85; 95% CI=1.69–2.01; 93 studies) compared to those rated by patients (d=0.67; 95% CI=0.49–0.85; 28 studies)...The difference between self-ratings and observer ratings was also found in the drug groups (self-rating d=1.12 versus observer rating d=2.89).

What does this mean? It could mean that psychiatrists tend to exaggerate small changes in their patients' depression. But it could mean that depression renders people unable to notice their own improvement. Perhaps the commonly used self-rating questionnaires, like the BDI, are just not very good at measuring depression, while observer rating scales, like the HAMD, are better. On the other hand it could be that self-rating scales are better, and observer-rating scales tend to exaggerate changes. Or...

Any or all of these could be true. Speaking as both a sufferer from depression and as a trained depression observer (I use the HAMD for research), I can confidently say that rating depression is one of the hardest things I ever have to do. Monitoring my own ups and downs, let alone putting a number on them, is extremely difficult. Trying to put a number on the mood of a patient who I've only known for an hour is even harder.

Poets and novelists struggle mightily to capture the purely qualitative aspects of our emotions. The idea that some guy reading a list off a printed list of questions could succeed at putting a number on a stranger's wellbeing in 5 minutes seems faintly absurd.

3. The results of this meta-analysis are much more favorable to antidepressants than was the analysis of Irving Kirsch et. al. (2008), Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration. This was the (in)famous paper that everyone in the media thought proved that "Prozac doesn't work".

Kirsch et. al. reported an average difference between the drug improvement and the placebo improvement of d=0.32, as against d=0.81 in this study. Conventionally, a standardized effect size d of 0.3 would be called "small" while 0.8 would be called "large". So this is a big difference. Why?

Again, there are plenty of possible reasons. Kirsch et. al. included fewer trials - only 35 -and only considered "newer" antidepressants. Rief et. al. included trials of older drugs. Kirsch et. al. included unpublished drug company data; Rief. et. al. only included published trials, meaning that publication bias could have been a problem (although they say that there is no evidence it was.)

Differences in the statistical techniques used could also explain it. As a series of outstanding posts by P J Leonard and Robert Waldmann last year showed, there were serious problems with the Kirsch et. al. analysis; these are too technical to go into here but suffice it to say that if the authors of this analysis had chosen to use Kirsch et. al.'s methods they might have reached very different conclusions.

4. The most important message of Rief et. al.'s analysis is also the simplest. There are vast differences between trials in terms of what happened to the patients.
This is a plot of the degree of improvement experienced by patients in the placebo group in each trial. The average improvement ranges from zero to huge. In addition, more recently published trials tended to find greater improvements. Yet all of these patients were given the exact same thing - sugar pills. (This is not a new finding.)

Clearly, something is seriously wrong here. People suffering from the same disease given the same treatment should show similar responses. The most likely explanation is that these groups of people were not all suffering from the same disease. The diagnosis of "major depression" is increasingly seen as problematic; almost certainly there is in fact no single disease called "depression" at all. Yet every antidepressant clinical trial operates under the assumption that there is one.

Given this, it's no wonder that antidepressants, and placebos, give such wildly different results in different trials. The wonder, perhaps, is that we are still conducting such trials without first establishing what exactly we think clinical depression is and how best to measure it.

[BPSDB]

Winfried Rief, Yvonne Nestoriuc, Sarah Weiss, Eva Welzel, Arthur J. Barsky, Stefan G. Hofmann (2009). Meta-analysis of the placebo response in antidepressant trials Journal of Affective Disorders DOI: 10.1016/j.jad.2009.01.029

Antidepressants, Placebos and the Failure of Psychiatry

Update 06 05 2009: Time readers may find this other post interesting!

Antidepressants are some of the most-prescribed drugs in the world. Yet they are also amongst the least well understood. We know little about how effective antidepressants are in the people who take them. Some antidepressants may work fantastically for most people. On the other hand some of them, perhaps all of them, may be useless or even worse. The truth is unclear.This is a minority view. Opinions about antidepressants are polarized - most people either firmly believe that they do work, or firmly believe that they don't. Yet neither of these positions seems to me to be supported by the evidence available. I don't think that anyone ought to firmly believe anything about these drugs - except that better research is urgently needed.

Another placebo meta-analysis

The issue is not a lack of studies. After fifty years of research, and untold millions of research dollars, there are hundreds of published clinical trials of antidepressants. It's when you try to make sense of the results of this great mass of trials that the problems become apparent. The latest attempt to do that is a paper from a German-American collaboration, Rief et. al.'s Meta-analysis of the placebo response in antidepressant trials. The authors set out to

Determine overall effect sizes of placebo and drug effects in antidepressant trials

In other words, they wanted to find out how much people improve when given antidepressants, and how much of that improvement is due to the placebo effect. They had plenty of data to work with. Even after discarding hundreds of trials for being too small or otherwise unsuitable:

The final sample consisted of 96 trials that reported sufficient data to compute effect sizes. The placebo groups of these studies comprised 9566 people. Approximately half of the studies were published after 1996, 68% were conducted in the United States, and the mean sample size was 86 participants.

And this is what they found after crunching the numbers:

The overall effect size [Cohen's d] of the placebo effect was 1.69 (95% CI=1.54–1.85), as compared to d=2.50 (95% CI=2.30–2.69) in the drug group. The ratio of the effect sizes suggests that 67.6% of the improvements in the drug group were attributable to the placebo effect [i.e. because 1.69 is 67.6% of 2.50].

That seems like a nice, neat and tidy result. When you give depressed people antidepressants, they get loads better (a standardized effect size of 2.50 is enormous), but most of that enormous improvement is due to the "placebo effect". However, the truth is not quite so neat.

It's a Little Bit More Complicated Than That

1. First off, none of the studies included in this analysis measured the placebo effect. The "placebo effect" is supposed to be the power of treatments to make people get better purely through making them expect to get better. It's certainly plausible that there could be big placebo effects in depression. There is plenty of anecdotal evidence that it happens.

In these studies, patients took either antidepressant pills or sugar pills. The patients given sugar pills were assessed as having got a lot better, on average. Is that evidence for the placebo effect? No, because as I've explained before, the improvement reported in the placebo group could be huge even if there were no "placebo effect" at all. The patients might have just got better spontaneously, because people who are depressed do tend to get better with time. It might have been that old chesnut, regression to the mean. Or maybe the patients only seemed to get better on average because the ones who didn't get better dropped out of the trial.

According to a meta-analysis of trials which actually did examine the placebo effect - by comparing people given placebos to people who got no treatment at all - the placebo effect in depression is at best small (Hrobjartsson & Gøtzsche 2004). However, the authors of this paper are well known for being very skeptical of placebos, and the number and quality of the trials was very low. There were 7 trials with a total of 258 patients. That's it. The only reasonable view is that we just don't know how powerful placebos are in depression.

2. Rief et. al. found that the size of the effects of antidepressants and placebos was much bigger when using "observer-rating" to measure the severity of depression, as compared to when patients rated their own symptoms. The difference between the two types of rating scale was enormous, dwarfing the drug vs. placebo difference:

In the placebo groups, there was a substantial difference between effect sizes for improvements rated by observers (d=1.85; 95% CI=1.69–2.01; 93 studies) compared to those rated by patients (d=0.67; 95% CI=0.49–0.85; 28 studies)...The difference between self-ratings and observer ratings was also found in the drug groups (self-rating d=1.12 versus observer rating d=2.89).

What does this mean? It could mean that psychiatrists tend to exaggerate small changes in their patients' depression. But it could mean that depression renders people unable to notice their own improvement. Perhaps the commonly used self-rating questionnaires, like the BDI, are just not very good at measuring depression, while observer rating scales, like the HAMD, are better. On the other hand it could be that self-rating scales are better, and observer-rating scales tend to exaggerate changes. Or...

Any or all of these could be true. Speaking as both a sufferer from depression and as a trained depression observer (I use the HAMD for research), I can confidently say that rating depression is one of the hardest things I ever have to do. Monitoring my own ups and downs, let alone putting a number on them, is extremely difficult. Trying to put a number on the mood of a patient who I've only known for an hour is even harder.

Poets and novelists struggle mightily to capture the purely qualitative aspects of our emotions. The idea that some guy reading a list off a printed list of questions could succeed at putting a number on a stranger's wellbeing in 5 minutes seems faintly absurd.

3. The results of this meta-analysis are much more favorable to antidepressants than was the analysis of Irving Kirsch et. al. (2008), Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration. This was the (in)famous paper that everyone in the media thought proved that "Prozac doesn't work".

Kirsch et. al. reported an average difference between the drug improvement and the placebo improvement of d=0.32, as against d=0.81 in this study. Conventionally, a standardized effect size d of 0.3 would be called "small" while 0.8 would be called "large". So this is a big difference. Why?

Again, there are plenty of possible reasons. Kirsch et. al. included fewer trials - only 35 -and only considered "newer" antidepressants. Rief et. al. included trials of older drugs. Kirsch et. al. included unpublished drug company data; Rief. et. al. only included published trials, meaning that publication bias could have been a problem (although they say that there is no evidence it was.)

Differences in the statistical techniques used could also explain it. As a series of outstanding posts by P J Leonard and Robert Waldmann last year showed, there were serious problems with the Kirsch et. al. analysis; these are too technical to go into here but suffice it to say that if the authors of this analysis had chosen to use Kirsch et. al.'s methods they might have reached very different conclusions.

4. The most important message of Rief et. al.'s analysis is also the simplest. There are vast differences between trials in terms of what happened to the patients.
This is a plot of the degree of improvement experienced by patients in the placebo group in each trial. The average improvement ranges from zero to huge. In addition, more recently published trials tended to find greater improvements. Yet all of these patients were given the exact same thing - sugar pills. (This is not a new finding.)

Clearly, something is seriously wrong here. People suffering from the same disease given the same treatment should show similar responses. The most likely explanation is that these groups of people were not all suffering from the same disease. The diagnosis of "major depression" is increasingly seen as problematic; almost certainly there is in fact no single disease called "depression" at all. Yet every antidepressant clinical trial operates under the assumption that there is one.

Given this, it's no wonder that antidepressants, and placebos, give such wildly different results in different trials. The wonder, perhaps, is that we are still conducting such trials without first establishing what exactly we think clinical depression is and how best to measure it.

[BPSDB]

Winfried Rief, Yvonne Nestoriuc, Sarah Weiss, Eva Welzel, Arthur J. Barsky, Stefan G. Hofmann (2009). Meta-analysis of the placebo response in antidepressant trials Journal of Affective Disorders DOI: 10.1016/j.jad.2009.01.029

More on Voodoo Correlations

I just want to draw your attention to Brad Buchsbaum's blog, where he has two truly excellent posts(1,2) on the Vul et. al. (2009) Voodoo Correlations in Social Neuroscience paper.Here's hoping there will be plenty more to follow...

My take on this paper is here.