Stories about new innovations in health appear almost daily in the media, but the claims are frequently overblown, misleading, or completely false. In a TED talk from July, 2011, journalist Ben Goldacre talks about how to spot and avoid bad science.
BROOKE GLADSTONE: From WNYC in New York, this is On the Media. I'm Brooke Gladstone. Bob Garfield is still away, but you’ll get a little taste of him later in this hour which, this holiday weekend, we’ve decided to devote to the festive theme of bad data, especially in medical reporting. We’ll talk about an information industry riddled with accidental and intentional error, dating from Ben Franklin and the Piltdown man to the current arguments over autism. We’ll consider a research business model built on painting by numbers, often wrong numbers.
And we’ll start with epidemiologist Ben Goldacre, author of Bad Pharma and Bad Science. He is, needless to say, one of bad data’s liveliest critics. In this excerpt from a 2011 TED Talk, he gives us a tour of the minefield of medical numbers. But be warned, he talks fast.
[JULY, 2011 TED TALK CLIP]:
DR. BEN GOLDACRE: I'm a doctor, but I kind of slipped sideways into research, and now I'm an epidemiologist. And nobody really knows what epidemiology is. Epidemiology is the science of how we know in the real world if something is good for you or bad for you. And it's best understood through example as the science of those crazy, wacky newspaper headlines. These are from the Daily Mail. Every country in the world has a newspaper like this. It has this kind of bizarre, ongoing philosophical project of dividing all the inanimate objects in the world, really, into the ones that either cause or prevent cancer.
So here are some of the things they said cause cancer recently: divorce, Wi-Fi, toiletries and coffee. Here are some of the things they say prevents cancer: crusts, red pepper, licorice and coffee. So already you can see there are contradictions here.
Coffee both causes and prevents cancer. And as you start to read on, you can see that maybe there's some kind of political valence behind some of this.
So for women, housework prevents breast cancer, but for men, shopping could make you impotent. So we know that we need to start unpicking the science behind this.
And what I hope to show is that unpicking dodgy claims, unpicking the evidence behind dodgy claims, isn't a kind of nasty carping activity. Real science is all about critically appraising the evidence for somebody else's position. That's what happens in academic journals. That's what happens at academic conferences. The Q&A session after a post-op presents data - is often a blood bath. And nobody minds that. We actively welcome it. It's like a consenting intellectual S&M activity.
So what I'm going to show you is all of the main things, all of the main features of my discipline, evidence-based medicine. And I will talk you through all of these and demonstrate how they work, exclusively using examples of people getting stuff wrong.
So we'll start with the absolute weakest form of evidence known to man, and that is authority. In science, we don't care how many letters you have after your name. In science, we want to know what your reasons are for believing something. How do you know that something is good for us or bad for us? But we're also unimpressed by authority because it's so easy to contrive. This is somebody called Dr. Gillian McKeith, PhD. She is our TV diet guru. She has massive kind of five series of prime time television, giving out very lavish and, and exotic health advice. She, it turns out, has a non-accredited correspondence course PhD from somewhere in America. She also boasts that she's a certified professional member of the American Association of Nutritional Consultants, which sounds very glamorous and exciting. You get a certificate and everything. This one belongs to my dead cat Hetti.
She was a horrible cat. You just go to the website, fill out the form, give them $60 and it arrives in the post. Now, that's not the only reason that we think this person is an idiot. She also goes and says things like, you should eat lots of dark green leaves, ‘cause they contain lots of chlorophyll and that will really oxygenate your blood. And anybody who's done school biology remembers that chlorophyll and chloroplasts only makes oxygen in sunlight, and it's quite dark in your bowels after you've eaten spinach.
Next, we need proper science, proper evidence. So, "Red wine can help prevent breast cancer." This is a headline from the Daily Telegraph in the UK "A glass of red wine a day could help prevent breast cancer." So you go and find this paper, and what you find is it is a real piece of science. It’s a description of the changes in the behavior of one enzyme when you drip a chemical extracted from some red grape skin onto some cancer cells in a dish on a bench in a laboratory somewhere. And that's a really useful thing to describe in a scientific paper, but on the question of your own personal risk of getting breast cancer if you drink red wine, it tells you absolutely bugger all. Okay?
So ideally, what you want to do is a trial. And everybody thinks they're very familiar with the idea of a trial. Trials are very old. The first trial was in the Bible, Daniel 1:12. It's very straightforward. You take a bunch of people, you split them in half, you treat one group one way, you treat the other group the other way, and then a little while later you follow them up and see what happened to each of them.
So I'm going to tell you about, about one trial, which is probably the most well-reported trial in the UK news media over the past decade. And this is the trial of fish oil pills. And the claim was fish oil pills improve school performance and behavior in mainstream children. And they said, "We've done a trial. All the previous trials were positive, and we know this one's gonna be too." That should always ring alarm bells, right, because if you already know the answer to your trial, you shouldn't be doing one. Either you've rigged it by design, or you've got enough data so there's no need to randomize people anymore.
So this is what they were going to do in their trial. They were taking 3,000 children, they were going to give them all these huge fish oil pills, six of them a day, and then a year later, they were going to measure their school exam performance and compare their exam performance against what they’d predicted their exam performance would have been if they hadn't had the pills.
Now, can anybody spot a flaw in this design?
And no professors of clinical trial methodology are allowed to answer this question. So there's no control. Okay, there’s no control group, but that sounds really techie, right? That sounds really – no, that’s a technical term. The, the kids got the pills, and then their performance improved. What else could it possibly be if it wasn't the pills?
It’s the placebo effect. The placebo effect is one of the most fascinating things in the whole of medicine. It's not just about taking a pill and your performance and your pain getting better. It’s about our beliefs and expectations. It's about the cultural meaning of a treatment. And this has been demonstrated in a whole raft of fascinating studies comparing one kind of placebo against another. So we know, for example, that two sugar pills a day are a more effective treatment for getting rid of gastric ulcers than one sugar pill a day. Two sugar pills a day beats one sugar pill a day. And that's an outrageous and ridiculous finding, but it's true.
So we know that our beliefs and expectations can be manipulated, which is why we do trials where we control against a placebo, where one half of the people get the real treatment and the other half get placebo.
But that's not enough. What I've just shown you are examples of the very simple and straightforward ways that journalists and food supplement pill peddlers and naturopaths can distort evidence for their own purposes. What I find really fascinating is that the pharmaceutical industry uses exactly the same kinds of tricks and devices, but slightly more sophisticated versions of them, in order to distort the evidence that they give to doctors and patients, and which we use to make vitally important decisions.
So firstly, trials against placebo: Everybody thinks they know that a trial should be a comparison of your new drug against placebo. But actually, in a lot of situations, that's wrong because often we already have a very good treatment that is currently available, so we don't want to know that your alternative new treatment is better than nothing. We want to know that it's better than the best currently available treatment that we have. And yet, repeatedly, you consistently see people doing trials still against placebo. And you can get a license to bring your drug to market with only data showing that it's better than nothing, which is useless for a doctor like me trying to make a decision.
But that's not the only way that you can rig your data. You can also rig your data by making the thing that you compare your new drug against really rubbish. You can give the competing drug in too low a dose, so that people aren't properly treated. You can give the competing drug in too high a dose, so that people get side effects. And this is exactly what happened with antipsychotic medication for schizophrenia.
Twenty years ago, a new generation of antipsychotic drugs were brought in and the promise was that they would have fewer side effects. So people set about doing trials of these new drugs against the old drugs, but they gave the old drugs in ridiculously high doses, 20 milligrams a day of haloperidol. And it's a foregone conclusion, if you give a drug at that high a dose, that it will have more side effects and that your new drug will look better.
Ten years ago, history repeated itself, interestingly, when risperidone, which was the first of the new-generation antipsychotic drugs, came off copyright, so anybody could make copies. Everybody wanted to show that their drug was better than risperidone, so you see a bunch of trials comparing new antipsychotic drugs against risperidone at eight milligrams a day. Again, not an insane dose, not an illegal dose, but very much at the high end of normal, and so you're bound to make your new drug look better.
And so it's no surprise that overall, industry-funded trials are four times more likely to give a positive result than independently sponsored trials. The negative data goes missing in action; it's withheld from doctors and patients. And this is the most important aspect of the whole story. It's at the top of the pyramid of evidence. We need to have all of the data on a particular treatment to know whether or not it really is effective.
This is a drug called reboxetine, and this is a drug which I myself have prescribed to patients. And I'm a very nerdy doctor. I hope I try to go out of my way to try and read and understand all the literature. I read the trials on this. They were all positive. They were all well conducted. I found no flaw.
Unfortunately, it turned out that many of these trials were withheld. In fact, 76 percent of all of the trials that were done on this drug were withheld from doctors and patients. Now, if you think about it, if I tossed a coin 100 times, and I'm allowed to withhold from you the answers half the times, then I can convince you that I have a coin with two heads. Okay? If we remove half of the data, we can never know what the true effect size of these medicines is.
And this is not an isolated story. Around half of all of the trial data on antidepressants has been withheld, but it goes way beyond that. The Nordic Cochrane Group were trying to get a hold of the data on that to bring it all together. The Cochrane Groups are an international nonprofit collaboration that produce systematic reviews of all of the data that has ever been shown. And they need to have access to all of the trial data. But the companies withheld that data from them, and so did the European Medicines Agency for three years. This is a problem that is currently lacking a solution.
And to show how big it goes, this is a drug called Tamiflu, which governments around the world have spent billions and billions of dollars on. And they spend that money on the promise that this is a drug which will reduce the rate of complications with flu. We already have the data showing that it reduces the duration of your flu by a few hours. But I don't really care about that. Governments don't care about that. We prescribe these drugs, we stockpile them for emergencies on the understanding they will reduce the number of complications, which means pneumonia and which means death.
The infectious diseases Cochrane Group, which are based in Italy, have been trying to get the full data in a usable form out of the drug companies, so that they can make a full decision about whether this drug is effective or not, and they've not been able to get that information.
[MUSIC UP & UNDER]
This is undoubtedly the single biggest ethical problem facing medicine today. All of these things are happening in plain sight, and they're all protected by a kind of force field of, of - of tediousness. And I think, with all of the problems in science, one of the best things that we can do is to lift up the lid, finger around at the mechanics and peer in. Thank you very much.