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Historical Interviews

[Editor’s Note: The appearance last summer of a highly critical article about epidemiology with multiple quotations from prominent epidemiologists and statisticians has been fueling discussions about the field ever since (Science, July 14, 1995, pp. 164 - 169). Examples of such discussions known to Epi Monitor include a departmental seminar that was held at Johns Hopkins and a student organized panel discussion at Harvard on the future of epidemiology. A special session to revisit this topic is planned for the upcoming SER meeting.

Given the severe nature of the criticisms and the importance of the topic for all epidemiologists, the Epi Monitor has reported on these events over the last several months. This month our continuing coverage takes this entire issue and goes behind the scenes to interview Gary Taubes, the Science correspondent who prepared the article. The interview provides valuable insights into the mind set of this national science reporter and helps epidemiologists to better understand the motivations and tactics of journalists. Also, the interview gives Mr. Taubes the opportunity to expand in detail about his views on the shortcomings of epidemiology. This interview gives epidemiologists a more in-depth understanding of his criticisms than is possible to get from the original Science article. The interview is lengthy; however, we believe our readers will get a good return on their investment. We welcome your comments and questions.]

Gary Taubes Faces Epidemiology

Epi Monitor: I think the readers will be interested in knowing something about your background and how you came to write this article about epidemiology. What part of the country are you from?

Taubes: Well, I was born in Rochester, New York and moved to Washington when I was 12. I went to college at Harvard and studied physics. I learned little. I got a C- in quantum physics, and my advisor suggested I try law as a career. I went on to get a Masters at Stanford in aeronautical engineering because, at the time, I thought I wanted to be an astronaut. I had always wanted to be an astronaut, but I weighed 220 pounds. I was a football player in college.

Epi Monitor: You were 220?

Taubes: Actually, I still weigh 220. There was little call for 220 pound astronauts in 1978, so I went to journalism school at Columbia. I wanted to do investigative reporting, but since I hadn’t worked for any newspapers, I couldn’t get any good jobs. Actually, I had three possibilities. One at the Dallas Morning News, one at CNN in Atlanta and one at Discover in New York. Visiting Dallas ruled out the Morning News, and CNN didn’t allow smoking in the newsroom, so I became a science writer by default.

Epi Monitor: So you started as a reporter at Discover magazine?

Taubes: Yes, in 1981. I started doing what every science writer does, which is writing about good science, scientific breakthroughs and the like. In 1984 I went off to Geneva to do a book about an experiment in high-energy physics at CERN. Carlo Rubia, who had already come up with the Nobel Prize qualifying discovery of two particles known as W and Z particles, claimed that he was onto a discovery that was even more important than that which everyone knew would get him the Nobel Prize, and which would be the greatest breakthrough in physics in 40 years.

Physics has a sort of accepted theory of the universe known modestly as the “standard model” and everything that had ever been discovered, every experimental finding that had ever been confirmed, had fit the standard model. For years physicists had been looking for physics beyond the standard model and Carlo claimed he had found it. It’s rare that somebody predicts a great discovery. So I asked if I could go and sit at the experiment and maybe write a book about it. He said okay, so I went off to Geneva in September 1984 and lived at CERN for eight months in a hostel. I tried to cover several hundred physicists and watched this great discovery vanish. As it turned out, the phenomena that Carlo thought were indicative of a great breakthrough were actually the product of parts per billion artifacts in his equipment and statistical fluctuations.

I wrote a book called Nobel Dreams, (Random House, New York, NY, 1987) and it was the beginning of my education in how hard it is to do good science. There were a lot of extremely bright people on these experiments, which cost tens of millions of dollars, and it was still extremely difficult for them to come up with the right answer.

Shortly thereafter, I wrote a piece for Discover magazine in which I talked about how hard it is to do good science. I counted up the number of discoveries that had been made in high energy physics between 1977 and 1987 and that had made it to the New York Times. It turned out there were 12 and nine of them later turned out to be wrong. The three that were right had been predicted by the standard model, so the physicists knew exactly what they were looking for.

Epi Monitor: These were all discoveries in physics?

Taubes: These were discoveries in high energy physics, which is a relatively clean experimental subject compared to epidemiology. Being predicted by the standard model is kind of equivalent to having biological plausibility, even strong biological plausibility.

Epi Monitor: Were you still working for Discover magazine when you came back from Geneva?

Taubes: Yes, I was a contributing editor. I started doing other pieces on controversial science. In March 1989, my publishers at Random House asked me if I wanted to write about cold fusion. It fascinated me because it seemed so obviously wrong. Here was this huge scientific controversy and a lot of scientists were putting their reputations on the line for something that was pretty obviously just dead wrong. I thought that was going to be an easy nine month book and I ended up spending three years on it and getting obsessed with it.

The book came out and was called Bad Science: The Short Life and Weird Times of Cold Fusion. (Random House, NY,NY, 1993) That actually got me into epidemiology because several of the physicists I got to know well while doing the book said, “well, if you think cold fusion is bad, you should look at electromagnetic fields from power lines and cancer.”

So, I started looking into that and found out that there were some amazing parallels between EMF issues and cold fusion and how bad science is propagated.

I also started questioning epidemiology because the EMF finding was based almost entirely on epidemiology. There’s this key paper in the field, which had gotten a lot of publicity; it had pretty much pushed people over the edge into believing that electromagnetic fields could cause leukemia. So, I was curious about this, and I sent a copy to Epidemiology editor, Ken Rothman, and he read it over. Ken said it was decent. He said it was good epidemiology. And the funny thing was, here was a paper which anyone who hadn’t gone in with any preconceived bias would have said was a null result. The investigators had over 600 possible associations of which they had found roughly 20 that were significant at the 95 percent confidence level. They would have expected 30 by chance alone. And they called this evidence of a positive association. I suddenly started thinking if this is good epidemiology, what’s the rest of the field like? In good experimental science, for instance, you’re not to throw out two-thirds of the data right off the bat because it’s negative. They threw out two-thirds of the data by trying three methods of classifying exposure and concentrating on only the one measure—calculated magnetic fields from power lines—that gave the results I just mentioned as though it were definitive evidence. To me, this violated everything I knew about good science. And as a matter of fact, it fit in perfectly with everything I had learned about “pathological science.”

Epi Monitor: What is the definition of pathological science?

Taubes: This is a term coined by Irving Langmuir, a Nobel prize winning chemist. Langmuir described pathological science as “the science of things that aren’t so,” and further stated that “these are cases where there’s no dishonesty involved, but where people are tricked into false results by a lack of understanding about what human beings can do to themselves in the way of being led astray by subjective effects, wishful thinking, or threshold interactions.” Then he gave symptoms of pathological science.

Epi Monitor: Let’s try to go back for a moment. You spent three years instead of nine months on the cold fusion story and wrote a book. When was Bad Science published?

Taubes: That came out in 1993.

Epi Monitor: Then what did you do?

Taubes: Then I went back to freelancing. I wrote a piece for the Atlantic Monthly on EMF called “Fields of Fear,” that was published in November 1994. And then from doing the electromagnetic fields piece, that’s what got me into wondering about epidemiology.

Epi Monitor: So now are you working more for Science or are you just freelancing?

Taubes: Well, I’m a correspondent for Science, so you could say I’m a contract writer.

Epi Monitor: And do you get to pick your own topics? How does that work?

Taubes: It’s a collaboration with the editors, but I tell them what interests me, like the epidemiology piece. I say “let me do a story about epidemiology because there’s a real interesting story here,” and they can agree or disagree about how interesting it is.

Epi Monitor: I’m not an expert in reading investigative reporting, but I can see that a lot of interviews and work went into that epidemiology piece. How long did it take to do that article?

Taubes: Well, I actually worked on it off and on for a year. It was a tough piece to write, obviously.

Epi Monitor: Are you writing any other books now?

Taubes: I’m looking for books. I would love to write a book on experimental science. I thought about doing a book on epidemiology. A book about things that won’t kill you, but...writing a book saying something will not kill you tends not to sell as well as saying that something will and the government is covering it up. On the other hand, to survive as a freelance writer, I have to generate some 50 to 100 story ideas a year which gets tiring, so the alternative is to do a book which has it’s own kind of misery, but there’s a lot more creative satisfaction to it...

I enjoy getting taken seriously by the scientific establishment. I’m not quite ready to just go out and write what I think the lay-public might gobble up. Although over the years, a physicist friend and I used to joke about creating a system of astrology based on quarks and writing a book on it so we could retire to Paris.

Epi Monitor: Some epidemiologists have said they believe you were primarily writing this article just to get attention. How do you respond to that?

Taubes: Well, this is a classic criticism of journalism. When I was doing this fusion book, I had cold fusion supporters saying the only reason I was knocking cold fusion was because that would sell better. Everybody says you do it for the money. The book ended up taking three years because I got obsessed with getting it right. By the time I was done, I was $30,000 in debt. You can't write for the money. If you write for the money it’s a lousy job. Especially anyone smart enough to do journalism well. Like anyone smart enough to do science well, you could have made more money going into business. You write because you’re more or less cursed with being a writer. There’s a lot of intellectual freedom to it. I wrote the epidemiology piece because I got fascinated with the question of its limits. One of the benefits of being a journalist is you can get paid to satisfy your curiosity, but you have to write about the end result.

I sit alone by myself in my apartment all day long which some people think is great, but if you imagine what it’s like to be alone for 10 years in your apartment, it starts to look not so great. I do a lot of stuff for the money and I like to live well. But once you start a story, the better the story—it’s like an obsession. It’s like a hunting dog. You follow it and follow it and you want to be able to answer every question. The problem with cold fusion is I wanted to know what happened. There’s this one crucial moment and I wanted to keep reporting until I knew exactly why everyone did what they did. In fact you almost have to do it until you know it so well that whatever decision they made seems inevitable. Especially when your two main subjects aren’t talking to you, which they weren’t in this case. Once you start this kind of investigation, there are a lot of similarities between journalism and science. You make a hypothesis and you test it and you have to try and tear down your results to see if you’re deluding yourself. You have to make sure you have the data to support your claims. You can’t over interpret the data. I’m always arguing with my editors in Science that they’re trying to over interpret data. I’ll say, this is what I have, that’s why there’s a caveat in there. Don’t take the caveat out because I can’t stand behind it without the caveat. You always have to doubt your own findings. You can’t fall in love with your results.

Epi Monitor: Speaking of not believing, you quote Sander Greenland as saying that “sinning is believing in your results.” Is not the reverse true? Is it sinning not to believe at some point? Particularly in epidemiology, if you cannot believe you cannot act, and if you cannot act then that is not public health.

Taubes: Well, this is the problem. This is what it all comes down to. I didn’t provide any real solutions in the epidemiology piece because I didn’t know any. Doing what I do is the easy part. I don’t have to provide solutions; I only have to criticize. And it’s very easy in epidemiology. My favorite quote about science, which I first saw sitting on the desk of a physicist at MIT is by an astronomer named Harlow Shapley at Harvard. He said that “a hypothesis or a theory is clear, decisive and positive, but it is believed by no one but the person who created it. Experimental findings, on the other hand, are messy, inexact things, which are believed by everyone except the person who did the work.”

It’s extraordinarily easy to be fooled when you’re doing experiments. This is what I was getting at in the epidemiology piece. Everything these guys are finding are subtle effects. You’ve got to be critical. Science can’t exist without critical thinking, without skepticism. You have to be critical of your own results. You have to try and prove you’re wrong. I know physicists who will spend two years trying to prove that the phenomena that they have apparently discovered in their experiment is actually an artifact or a fluctuation. Only when they fail to prove that that’s the case will they publish. This is how I learned experimental physics should be done. Now, physics isn’t epidemiology. We know that. You run your experiment, you get some signal, and you assume that nature and God or whoever are conspiring to make you make a fool of yourself. So you spend however long, six months, a year, trying to find out how that signal is phony. Is it an artifact or statistical fluctuation? So, you spend your six months trying to prove you’re wrong and then if you can't figure out how you screwed up, then you hold a seminar. You still haven’t thought about writing a paper yet, you hold a seminar and you might hold a dozen seminars in different places and say, look I did this experiment, I got this silly signal here that I can’t get rid of and could you guys show me how I’m wrong. And then if nobody can do it, nobody can show you where your mistake is, then you publish a paper. Finally— this might be two years later—the signal might be the greatest discovery in the history of physics, but you’re only going to say “experimental evidence shows...” and then you’re going to stick a question mark at the end. By the time this is all done, you’re always working from the assumption that you're wrong because the odds are very good you're wrong, if history is any indication. And still with all that, by the time these physicists get to the point where they publish a paper that gets into the New York Times, 75 percent of the results are wrong, and 100 percent of those that don’t agree with the standard model.

There’s a book called Reliable Knowledge by an Australian physicist and historian of science named John Ziman. He describes the front line of scientific research as the place not to find believable results. He describes it as the place “where controversy, conjecture, contradiction, and confusion are rife.” Then he writes “the physics of undergraduate text books is 90 percent true; the contents of the primary research journals of physics are 90 percent false. The scientific system is as much involved in distilling the former out of the latter as it is in creating and transferring more and more bits of data and items of information.”

Epi Monitor: A recent book makes the point that scientists criticize the legal profession all the time for the way they address things and how they reach conclusions. However, science has a lot more in common with the law than what most people think. Part of the similarity is the construction over time of a body of knowledge.

Taubes: It is somewhat the’re building up a body of knowledge, but the stuff that comes out at the front end is almost invariably wrong. This whole thing with epidemiology came down to something that could be described as the “best we could do” defense. Epidemiologists would tell me, “we know that our results are likely to be wrong and we also know that they’re going to get into the press anyway. It isn’t our fault that they get into the press or it isn’t our fault that the press over interprets them. There’s nothing we can do about it. Once we publish, what are we supposed to do about it?”

It’s a given that epidemiologic results get into the press. You know that. You know if you write a paper saying that your study suggests that some risk factor might cause some disease, it’s going to get into the press. You have to figure out a way—the field in general has to figure out a way—to stop it from getting into the press.

We’re going to ramble a little bit. “Smoking study sees risk of cancer of the breast” was the headline in the New York Times, May 5th. Here a study comes out that looks at whether or not cigarette smoking could cause breast cancer. And it finds not only an association, but a dose response.

Hill’s second criteria for causality was consistency of studies which is what you can describe as consensus. Here you had a consensus showing that smoking didn’t cause breast have 20 papers showing no association, and the 21st that shows an association makes the newspaper. The reporter says, “although it cannot now be said that the new conclusions come closer to the truth than those reached by the 20 other research groups that examined active smoking linked to breast cancer, the current epidemiologist believes his analytic approach has yielded a more accurate result.”

For starters, the reporter should have said, it’s unlikely that these conclusions come closer to the truth because you’ve got, if nothing else, odds of 20 to one against it. Then you’ve got the epidemiologist who believes his analytic approach has yielded a more accurate result. You’ve now got the experimentalist defending his own study. You’ve got him believing it, which is not good science. And the reporter then says, “the investigator attributed previous failures to detect a relation between active smoking and breast cancer to...” and then he goes on to suggest reasons why the other esearchers may have obtained the wrong results. Everything I learned about experimental science suggests that a good scientist would criticize his own study and assume he’s wrong because if he goes into this assuming he’s right he’s going to delude himself.

Epi Monitor: He is going to fall victim to pathological science?

Taubes: Yes. Although it’s possible that he did criticize his own results. He might have spent two hours discussing why he might be wrong and five minutes on why the other studies might be wrong, and the reporter only chose the latter. So it’s also possible that it was reported incorrectly. Either way, we now have a controversy. The fact that you’ve got consensus saying that there’s no association, you’ve got a 20 to one situation which the press has now turned into a 50/5O proposition and someone’s going to have to spend millions of dollars bringing it back to whatever the right answer is. And at the end, like I said, no matter how many negative results you get, it only takes one positive result to create a controversy. It’s a fascinating phenomenon at work here.

Epi Monitor: It is, and I have not really heard epidemiologists talk much about this. You talked about an unholy alliance in your Science article between the press, the universities and the investigators. There is a self-serving reason why these groups are more interested in the positive finding. Maybe all parties need to recognize this tendency and to set up safeguards to protect against it. What do you think?

Taubes: This is what’s interesting. I talked a lot to Harvard epidemiologist, Jamie Robins, about this and he was the only one that really got it. It’s conceivable that every force in epidemiology pushes toward the positive result. This is true of any science. That’s the danger. You get a negative result, you are not going to get more funding to pursue it, you have to think up another line of research. Everything pushes you to wanting to find something. This is what Langmuir talked about with “what human beings can do to themselves in the way of being led astray.” So now let’s say you want to measure that effect. You take every epidemiological study ever done and plot the risk ratios and say okay, now we have all these studies and at what point do the findings start getting real? At what point do the risk ratios start getting real? Is there any way to calibrate epidemiology? So you could say well, 95 percent of the time a risk ratio of two turns out to correlate with a null result. The problem is unless you have biological confirmation there’s no way to calibrate. You need the biological data to say this is a real result. But it’s conceivable that the true null result is up around two or three or four or six because you’ve got such a huge bias pushing people to go positive and reject the negative findings. It would be a fascinating study to try to do and I had talked to Jamie about doing it. One of the major problems with epidemiology is there’s no calibration. You don’t know. You can’t say, here’s my zero point, because nobody knows where the zero is. You don’t know how many negative results are getting thrown out. You don’t know how many positive results are being skewed.

Everybody talks about artifacts and biases and how they understand those and deal with them but it’s all very theoretical, and the field doesn’t have the checks and balances that other sciences have. I hate to keep bringing up physics,because I know when I interview these epidemiologists it would make their skin crawl to have to live up to the standards of physicists, but if I know that 75 percent of the results in physics are wrong and they have high standards—they have standards that epidemiologists bridle at being asked to meet—what percentage of the results are wrong in epidemiology? Isn’t that something you’d like to know?

Epi Monitor: Yes, I guess so. But remember that the subject matter of epidemiology is not some artificially created reality produced by a multi-million dollar accelerator. Epidemiology may have its limitations, but in the end it is studying the real world.

Taubes: Well, what constitutes an artifact in a physics experiment is the equivalent of a bias or a confounder in an epidemiology experiment.

Epi Monitor: I am not sure. Biases in epidemiology can dilute an effect, but the effect can still be real. In physics, you may create something which is completely artifactual.

Taubes: That’s the problem. You never know. When I talked to all these epidemiologists, they kept bringing up the same point. Any mismeasurement of exposure, they told me, is only going to work to make the effect smaller than it really is. Rothman explained this to me over and over again and I finally managed to understand this concept. I’m willing to accept that. Ergo, every time you see a signal, if there was any mismeasurement of exposure, it can only possibly be larger: therefore, you have to pursue it and you have to take it seriously.

But then you ask—which I did in the article—give me examples in the history of epidemiology where you started out with a small signal right on the borderline of noise, and then proceeded to come to a better understanding of all your biases and confounders and the signal got bigger to the point where it was undeniable. Nobody could give me an example. Actually, I got one and I put it in the article. But just one.

There’s an unspoken law of physics that’s relevant here. It comes from a physicist named Wolfgang Panofsky who is a brilliant scientist, who founded the Stanford Linear Accelerator Center, and who has also been involved for decades with science policy and defense technology policy. Panofsky’s law was if you throw money at an effect and if it doesn’t get bigger it means it’s not really there. Now that means if you see a potential signal, something just at the edge of your experimental resolution, and you do repeated experiments to try to isolate that phenomenon, to increase the signal to noise ratio, and that effect stays right at the limits of your resolution, right at the noise level, it means it’s noise. It’s not really there. It doesn't exist.

This is what happened in cold fusion. As the experiments got better and better and the error bars got smaller and smaller, the signal they professed to see got smaller and smaller as well. In epidemiology, if you throw money at an effect and it doesn’t get any bigger, you do a meta analysis! This is true. Take second-hand cigarette smoke. The argument is that you’ve done 30 or 50 studies. The reason you believe it is because the effect stays the same size. But now a physicist would tell you that means it’s not there. That means what you’re seeing is a combination of noise and wishful thinking, and self-delusion because you should have been able to figure out by now how to do the experiment better so that the signal to noise ratio improves.

What I’ve been trying to struggle with is just this question: the fact that to do the meta analysis and it suggests a positive result, doesn’t mean that the association really exists. I realize that epidemiology is much more difficult experimentally than physics, that the world of human beings is much messier than the world of elementary particles. But in physics, by the time you have to do the meta-analysis you already admit that the effect is not there. In epidemiology, you do the meta-analysis, and conclude that the effect is there. So what’s going on here? I don’t get it. I haven’t got a clue what the answer is. I find the question fascinating and I think epidemiologists should address it.

What epidemiologists are doing may be pathological science. It’s conceivable since it fits a lot of the symptoms of pathological science. For example, just being able to throw out negative results in the search for the perfect exposure classification. But, by doing that, you invite yourself into pathological science. You’ve now thrown out one of the key elements of defense— epidemiologists throw out a lot of the “immune system” of science in their pursuit of positive signals. And the rationale, of course, is that “people are dying out there.” This always reminds me of the film Jurassic Park when the characters keep repeating that phrase, “people are dying out there.” We can’t get too critical of our results because people are dying out there. We should therefore accept everything as a potential hazard. Every statistical fluctuation, every 95 percent confidence level finding has to be taken seriously because “people are dying out there.”

Epi Monitor: Well, obviously that’s not practical.

Taubes: Then the question becomes, how much money are you wasting with false positive results? Like electromagnetic fields and cancer. This country spends a billion dollars a year, by some estimates several billion a year, trying to ameliorate the effects of electromagnetic fields. That’s the cost to society because epidemiologists “went off the rails.” With the help of a very powerful journalist, how many other examples are there like that?

Epi Monitor: I don’t know if this is a fair question, but did you have a goal in mind in writing the Science article?

Taubes: Well, I wanted to find out if I was right or in essence I wanted to be convinced that I was wrong and nobody managed to come close to convincing me.

Epi Monitor: That you were wrong?

Taubes: That I was wrong about the similarity between epidemiology and pathological science, that I was wrong about how close epidemiology comes to pathological science and how dangerous that might be. And in essence the answer to your question about why did I do so much reporting, why did I talk to so many people, is because I kept searching for someone who could convince me that I was wrong and I kept creating hypotheses and looking for people I could test them with.

Epi Monitor: So you were trying to prove yourself wrong and you don’t think you did.

Taubes: No, I didn’t. I’m still open to being proved wrong and it's still conceivable that I just don't understand that there's something about the way epidemiology is done that makes it so different from what I’ve learned, that I haven’t managed to apply my understanding of experimental science properly to epidemiology.

Epi Monitor: Assuming epidemiologists disagree with you, what is the difference in their opinion between physics and epidemiology that explains why epidemiology is not pathological science?

Taubes: The funny thing is that the epidemiologists did agree with me! They answer, “we know this already.” I wasn’t writing this article for the best epidemiologists.

Everything I knew I was told by the people I interviewed. Yes, it’s true that I select my quotes to back the points I want to make, which is a way of saying I select the data I want. But on the other hand, I wasn’t out to write a paper about the victories of epidemiology. There have been some victories and they were properly credited.

Epi Monitor: That was one of the criticisms of your article. Epidemiologists said it is unbalanced and that you were only talking about our warts. What about our victories?

Taubes: Well, what I am saying is the warts are huge. The victories are few, and at this point, a whole field may be on the verge of propagating pathological science, which means they cannot get good enough resolution to identify the effects they’re studying. Epidemiologists may be seeing and reporting that there are canals on Mars because they’re looking at Mars through Galileo’s telescope. And that’s the nature of the field and all the statistical wizardry in the world isn’t going to change that because the experimental subjects are messy and the artifacts and biases found are so huge and the signals are small. Epidemiologists have to be willing to confront that. That’s the problem.

Anyway, I was writing for the press. That’s my answer to your question about my goal. I was writing to my colleagues in the press saying to them, “would you please stop treating these epidemiological studies as definitive? You're writing about speculation as though it's definitive.” When I grew up I had a Jewish mother who would always tell me that “they” say this and “they” say that. You know that argument? “They” say that drinking coffee is bad for you. I used to ask, who's “they” for Christ’s sakes? Are “they” the best scientists in the world? Are “they” doing good science? This was my answer to the press and I wanted to say, “stop quoting ‘they.’ Start looking at these studies critically, and report them accurately and in context.”

Epi Monitor: Do you think Science was a good vehicle for achieving your purpose?

Taubes: I would like to think that all my science reporting colleagues and all the health reporters in the world read Science. But in fact, they probably don’t. A good friend of mine is a science editor at one of the most influential newspapers in the world and he tells me he doesn’t have time to read Science, and he used to work for Science before he went to this newspaper. So, is it a good vehicle? No, but, what is? There’s a huge gap between reality and understanding and the best epidemiologists know about the gap. That’s why I can quote them.

Epi Monitor: I had the sense in reading your article that many of the epidemiologists you quoted had been ambushed. Let me explain what I mean. The epidemiologists probably spoke to you in such a way that you were both in agreement. But bringing out these opinions in public, and quoting them this way where you had them being self-critical of their own discipline meant that they were in an awkward position.

Taubes: Oh yes, sure. But they’re supposed to be critical of their own discipline. In a way, I was making them look good. I was making them look like good scientists.

Epi Monitor: Epidemiologists are not expected to be critical of their discipline but of the results of epidemiologic work.

Taubes: You’re supposed to be critical of everything! I’ve never seen a field where people are saying, “My God we’ve got to be more outwardly supportive otherwise we could lose funding.”

Let’s talk about the ambush...Actually, before the article was published one of the epidemiologists who read it in draft, told me it was like when you tell your mistress what kind of problems you have with your wife, but you’re not telling your mistress for publication. And when push comes to shove, you stay with your wife. There’s a lot of validity to that.

Epi Monitor: About those quotes you got. I had the feeling that you had this grand vision in mind, this grand hypothesis, this grand cathedral. The epidemiologists were talking to you and providing quotations and had no idea of what you were constructing. They were providing bricks along the way not knowing about the cathedral. And then the article appears in print and all these bricks fit into place beautifully and it looks like all these epidemiologists were collaborators with you in building the cathedral. But in reality, they were never really such willing collaborators and would not have been if they would have seen the cathedral.

Taubes: That’s true, that’s very true. It’s a valid criticism and I admit it. On one hand, it’s obvious. I’m writing the article. It’s always going to be my grand cathedral, in which I synthesize what I have learned and put it down on paper in a way that makes the strongest possible case. On the other, and this is more subtle, as a journalist for Science, as a lousy reporter for Science, I’m not allowed to editorialize. Therefore, I have to get other people to say what I want them to say. Or I have to find other people who will say what I want them to say. So you hit it right on the head. That’s a very valid criticism. It was my grand cathedral, it was my vision. And I have said this to people, it’s ironic because I’m only a lousy reporter, I’m not a Harvard professor, I don’t have that stamp of authority. I have to get people to agree with me. That’s one side to it. For instance, I couldn’t mention pathological science in the article because none of the epidemiologists knew about it even though I started sending it to people, hoping that one of them would say, “gee this is interesting,” and make the comparison for me.

The other side is when it’s all done, I send it to my sources to read and critique... three and maybe four, had read my article in draft prior to publication...People had a chance to look at the cathedral and say this is wrong.

Epi Monitor: That’s hard to believe. That some people would say the kinds of things that were quoted and not seek to have them removed prior to publication if they were given the chance.

Taubes: Well, they also had the intellectual integrity to stand by what they said, even if in the long run they might come to regret it. As for those people who say that I don’t say enough good things about epidemiology, if they’ll read the article closely, they’ll find I talk about what drives the epidemiological quest, and that epidemiology is the best way to identify these risk factors.

There are things I added in the article and they were added after people read the draft, about the population studies for instance. That was added after people read the draft because they said, “you’re not pointing out why we believe what we believe.”

Epi Monitor: What do you mean by the “population studies?”

Taubes: Why epidemiologists believe that most of these diseases are caused by factors in the environment that can be identified or hopefully can be identified, about what drives the epidemiologic quest for risk factors.

Epi Monitor: That’s really the rationale for the field, not the victories.

Taubes: Yes. I don’t deny that there’s a rationale for the field.

Epi Monitor: The question I want to ask you is the essential question in my mind: is this a problem of implementation and not a problem inherent to epidemiology per se? One could conclude from your article that epidemiology is so ill-equipped to meet the challenge that it does more harm than good and we would all be better off if epidemiologists re-programmed and went into something else.

Taubes: Not all of them, but maybe some or most. It’s interesting; Ernst Wynder, who is president of the American Health Foundation, had an article in the American Journal of Epidemiology (p. 747 Vol. 143, November 8, 1996), responding to my article, in which he says I failed to recognize that “in epidemiology, as in other branches of science, there are good as well as inadequate studies and inappropriate inferences.” Of course, I recognize it. I say it over and over. He says I’m damning the whole field. I’m saying that there’s a lot of bad epidemiology and it makes the press and it’s tolerated.

Epi Monitor: To put it another way, the subheadline to your article reads: “the search for subtle links is an unending source of fear but often yields little certainty.” So the question is, should we stop the search?

Taubes: Well, no. The point is it has to be done right and it has to be done like a science. And it’s got to have the rules of an experiment.

Alvin Feinstein said this in Science back in 1988. Feinstein said effectively the same thing I did although he put it more scientifically. A firestorm of criticism arose and people attacked Feinstein. Feinstein makes mistakes, Feinstein’s sloppy, Feinstein’s this and that. The fact is as far as I can tell, his examples might have been wrong, but his criticisms were not that far off base. They fit what I know about experimental science. He basically said that in a lot of epidemiology you throw out the basic premises of experimental science because if you include them you won’t find an association, or if you include them, you won’t get funding.

Epidemiologists seem to be remarkably tolerant of this sloppiness. But the fact is, I can name you every physicist who made a major mistake on a paper, who published a discovery that was wrong. And these guys are never allowed to forget it. The guy who came up with the split A-2 in the sixties has been out of physics for years because of his mistake. Elliott Bloom, who came up with the Zeta particle in 1984... Elliott is a great physicist, yet he still can’t get a drink with his friends without them reminding him about the damn Zeta article. They’re vicious and they’re vicious for a reason. There’s an infinite number of wrong results. There’s only that many right ones. You need that criticism. You need to be afraid to publish a wrong result.

Epidemiologists, on the other hand, produce wrong results every day and they give the same defense. If it takes five years to do a study, if the data stink, what are we going to do? You can’t get funding just to do another five years. You can’t get the multi-millions of dollars. And all the time people are dying out there. “We’ve got to publish these dubious results, because it’s the best we can do.”

You know, in cold fusion—the worst scientists would tell me, this is a classic line—“Sure our data stink, but give us the money and we’ll do the experiment right.” This is an argument that bad scientists use all the time. “Okay, I know that I’m claiming a discovery here and I know that my experiment stinks, but now that I’ve claimed the discovery, give me the funding so I can do it right.” This is bad science.

Epi Monitor: The claim to asking for more money is to say that you screwed up one time so bet on me again?

Taubes: No. The good scientists do the experiment right before they publish, even if it takes years. You work on it for ten years and you make sure that what you publish is believable. That doesn’t seem to be a criteria in epidemiology. Instead, epidemiologists say this is the best we could do. So we’re going to publish the best we can do, the data are poor, the interpretations are bit of a stretch, but let’s be serious. If I work on it of course I can make the association go away if I want to. But what if it’s real? If I make it go away I’m not going to get the funding to do the experiment right to get the sufficient data so that I can come up with the believable results. I’m going to publish a study in which the data are borderline, the interpretation is even more borderline, but by doing it I’m going to be able to say, “look, there might be an effect here, please give me the money so I can do it correctly.”

Epi Monitor: Not that you can do it better necessarily but that you can pursue it further. Is that what you mean?

Taubes: Yes, so I can pursue it further. Well, ideally so that I can get a large enough sample size to come to a meaningful conclusion.

Epi Monitor: The first assumption in your story is that there is an “epidemic of anxiety” caused by conflicting epidemiologic results. What evidence do you have that these conflicting results are really causing us a problem? Maybe we have to tolerate a few false positives to get to where we want to go.

Taubes: That may be true, but tell that to Dow-Corning. Tell that to the guy who owns the house on the power lines who can’t sell the house. Tell that to the makers of saccharin. These are interesting issues, but the point is, once studied, something becomes guilty until proven innocent.

Epi Monitor: Does this constitute “epidemiologic malpractice?”

Taubes: Well, everyone makes mistakes. Brian MacMahon came up to me after a lecture I gave at Harvard on this subject and said, “you probably think we’re pathological scientists for the mistake on pancreatic cancer and coffee consumption.” Everybody in science makes mistakes. That doesn’t bother me. The point is you’ve got to understand how easy it is to make mistakes and how to keep those probable mistakes away from my colleagues in the press.

Epi Monitor: That’s an interesting point. Given that we are almost guaranteed to get press, that there is a sort of built-in interest in what we do, should that add an extra level of precaution?

Taubes: You should be even that much more cautious. Let’s assume Ziman was correct about epidemiology, too, and 90 percent of the results in the research journals of epidemiology is wrong. The 90 percent in the physics journals that’s wrong is not going to make the papers. Nobody cares about it. But the 90 percent in the epidemiology journals do make the press. And once they’re out there they don’t go away. I can’t put Sweet n’ Low in my coffee without one of my friends saying “you're killing yourself.” And it’s one of the few times that epidemiology ever made an effect go away!

Criticism is never bad, it’s necessary. The fact is, what I did in my article I shouldn’t have to do. It should be done in the profession. The epidemiologists know what’s wrong with the field, they know there’s a lot of junk out there, they know there’s a lot of “do-gooder epidemiologists” out there who think that the goal of being an epidemiologist is to indict a chemical and sink some nasty chemical company. And I didn’t come to that conclusion without help. It was epidemiologists who first said this to me.


What it all comes down to is this: what is the possibility that a lot of epidemiology is pathological science? The problem is that you’ve got to understand what pathological science is to recognize it. And the reason it’s called pathological is because it’s tricky, it’s hard to diagnose. There’s nothing easy about recognizing it. And if you are doing pathological science, a) why, and b) how do you stop it. I’m not saying all of epidemiology is pathological, but I bet you a lot of it is. Once you start throwing out those defense mechanisms and rationalizing away why you can’t be so critical, why you can throw out negative results, once you start making excuses, you open the door to publishing and to pushing a lot of junk and it gets expensive. I don’t know how expensive, I don’t know what the risk is to society. How much bad science do you have to allow so good science can get done? I can’t answer those questions.

Published June 1996 


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