In many scientific studies, investigators monitor disease rates and lifestyle factors (diet, physical activity, prescription drug use, exposure to pollutants, etc.) in or between large populations. They then try to infer conclusions — i.e., hypotheses — about what caused the disease variations observed. Because these studies can generate an enormous number of speculations about the causes or prevention of chronic diseases, they provide the fodder for much of the health news that appears in the media — from the potential benefits of fish oil, fruits and vegetables to the supposed dangers of sedentary lives, trans fats and electromagnetic fields. Because these studies often provide the only available evidence outside the laboratory on critical issues of our well-being, they have come to play a significant role in generating public-health recommendations as well.

The dangerous game being played here, as David Sackett, a retired Oxford University epidemiologist, has observed, is in the presumption of preventive medicine. The goal of the endeavor is to tell those of us who are otherwise in fine health how to remain healthy longer. But this advice comes with the expectation that any prescription given — whether diet or drug or a change in lifestyle — will indeed prevent disease rather than be the agent of our disability or untimely death. With that presumption, how unambiguous does the evidence have to be before any advice is offered?

The catch with observational studies like the Nurses’ Health Study on H.R.T, no matter how well designed and how many tens of thousands of subjects they might include, is that they have a fundamental limitation. They can distinguish associations between two events — that women who take H.R.T. have less heart disease, for instance, than women who don’t. But they cannot inherently determine causation — the conclusion that one event causes the other; that H.R.T. protects against heart disease. As a result, observational studies can only provide what researchers call hypothesis-generating evidence — what a defense attorney would call circumstantial evidence.

Testing these hypotheses in any definitive way requires a randomized-controlled trial — an experiment, not an observational study — and these clinical trials typically provide the flop to the flip-flop rhythm of medical wisdom. Until August 1998, the faith that H.R.T. prevented heart disease was based primarily on observational evidence, from the Nurses’ Health Study most prominently. Since then, the conventional wisdom has been based on clinical trials — first HERS, which tested H.R.T. against a placebo in 2,700 women with heart disease, and then the Women’s Health Initiative, which tested the therapy against a placebo in 16,500 healthy women. When the Women’s Health Initiative concluded in 2002 that H.R.T. caused far more harm than good, the lesson to be learned, wrote Sackett in The Canadian Medical Association Journal, was about the “disastrous inadequacy of lesser evidence” for shaping medical and public-health policy. The contentious wisdom circa mid-2007 — that estrogen benefits women who begin taking it around the time of menopause but not women who begin substantially later — is an attempt to reconcile the discordance between the observational studies and the experimental ones. And it may be right. It may not. The only way to tell for sure would be to do yet another randomized trial, one that now focused exclusively on women given H.R.T. when they begin their menopause.

No one questions the value of these epidemiologic studies when they’re used to identify the unexpected side effects of prescription drugs or to study the progression of diseases or their distribution between and within populations. One reason researchers believe that heart disease and many cancers can be prevented is because of observational evidence that the incidence of these diseases differ greatly in different populations and in the same populations over time. Breast cancer is not the scourge among Japanese women that it is among American women, but it takes only two generations in the United States before Japanese-Americans have the same breast cancer rates as any other ethnic group. This tells us that something about the American lifestyle or diet is a cause of breast cancer. Over the last 20 years, some two dozen large studies, the Nurses’ Health Study included, have so far failed to identify what that factor is. They may be inherently incapable of doing so. Nonetheless, we know that such a carcinogenic factor of diet or lifestyle exists, waiting to be identified.

These studies have also been invaluable for identifying predictors of disease — risk factors — and this information can then guide physicians in weighing the risks and benefits of putting a particular patient on a particular drug. The studies have repeatedly confirmed that high blood pressure is associated with an increased risk of heart disease and that obesity is associated with an increased risk of most of our common chronic diseases, but they have not told us what it is that raises blood pressure or causes obesity. Indeed, if you ask the more skeptical epidemiologists in the field what diet and lifestyle factors have been convincingly established as causes of common chronic diseases based on observational studies without clinical trials, you’ll get a very short list: smoking as a cause of lung cancer and cardiovascular disease, sun exposure for skin cancer, sexual activity to spread the papilloma virus that causes cervical cancer and perhaps alcohol for a few different cancers as well.

Richard Peto, professor of medical statistics and epidemiology at Oxford University, phrases the nature of the conflict this way: “Epidemiology is so beautiful and provides such an important perspective on human life and death, but an incredible amount of rubbish is published,” by which he means the results of observational studies that appear daily in the news media and often become the basis of public-health recommendations about what we should or should not do to promote our continued good health.

In January 2001, the British epidemiologists George Davey Smith and Shah Ebrahim, co-editors of The International Journal of Epidemiology, discussed this issue in an editorial titled “Epidemiology — Is It Time to Call It a Day?” They noted that those few times that a randomized trial had been financed to test a hypothesis supported by results from these large observational studies, the hypothesis either failed the test or, at the very least, the test failed to confirm the hypothesis: antioxidants like vitamins E and C and beta carotene did not prevent heart disease, nor did eating copious fiber protect against colon cancer.

The Nurses’ Health Study is the most influential of these cohort studies, and in the six years since the Davey Smith and Ebrahim editorial, a series of new trials have chipped away at its credibility. The Women’s Health Initiative hormone-therapy trial failed to confirm the proposition that H.R.T. prevented heart disease; a W.H.I. diet trial with 49,000 women failed to confirm the notion that fruits and vegetables protected against heart disease; a 40,000-woman trial failed to confirm that a daily regimen of low-dose aspirin prevented colorectal cancer and heart attacks in women under 65. And this June, yet another clinical trial — this one of 1,000 men and women with a high risk of colon cancer — contradicted the inference from the Nurses’s study that folic acid supplements reduced the risk of colon cancer. Rather, if anything, they appear to increase risk.

The implication of this track record seems hard to avoid. “Even the Nurses’ Health Study, one of the biggest and best of these studies, cannot be used to reliably test small-to-moderate risks or benefits,” says Charles Hennekens, a principal investigator with the Nurses’ study from 1976 to 2001. “None of them can.”

Proponents of the value of these studies for telling us how to prevent common diseases — including the epidemiologists who do them, and physicians, nutritionists and public-health authorities who use their findings to argue for or against the health benefits of a particular regimen — will argue that they are never relying on any single study. Instead, they base their ultimate judgments on the “totality of the data,” which in theory includes all the observational evidence, any existing clinical trials and any laboratory work that might provide a biological mechanism to explain the observations.

This in turn leads to the argument that the fault is with the press, not the epidemiology. “The problem is not in the research but in the way it is interpreted for the public,” as Jerome Kassirer and Marcia Angell, then the editors of The New England Journal of Medicine, explained in a 1994 editorial titled “What Should the Public Believe?” Each study, they explained, is just a “piece of a puzzle” and so the media had to do a better job of communicating the many limitations of any single study and the caveats involved — the foremost, of course, being that “an association between two events is not the same as a cause and effect.”

Stephen Pauker, a professor of medicine at Tufts University and a pioneer in the field of clinical decision making, says, “Epidemiologic studies, like diagnostic tests, are probabilistic statements.” They don’t tell us what the truth is, he says, but they allow both physicians and patients to “estimate the truth” so they can make informed decisions. The question the skeptics will ask, however, is how can anyone judge the value of these studies without taking into account their track record? And if they take into account the track record, suggests Sander Greenland, an epidemiologist at the University of California, Los Angeles, and an author of the textbook “Modern Epidemiology,” then wouldn’t they do just as well if they simply tossed a coin?

As John Bailar, an epidemiologist who is now at the National Academy of Science, once memorably phrased it, “The appropriate question is not whether there are uncertainties about epidemiologic data, rather, it is whether the uncertainties are so great that one cannot draw useful conclusions from the data.”

Understanding how we got into this situation is the simple part of the story. The randomized-controlled trials needed to ascertain reliable knowledge about long-term risks and benefits of a drug, lifestyle factor or aspect of our diet are inordinately expensive and time consuming. By randomly assigning research subjects into an intervention group (who take a particular pill or eat a particular diet) or a placebo group, these trials “control” for all other possible variables, both known and unknown, that might effect the outcome: the relative health or wealth of the subjects, for instance. This is why randomized trials, particularly those known as placebo-controlled, double-blind trials, are typically considered the gold standard for establishing reliable knowledge about whether a drug, surgical intervention or diet is really safe and effective.

But clinical trials also have limitations beyond their exorbitant costs and the years or decades it takes them to provide meaningful results. They can rarely be used, for instance, to study suspected harmful effects. Randomly subjecting thousands of individuals to secondhand tobacco smoke, pollutants or potentially noxious trans fats presents obvious ethical dilemmas. And even when these trials are done to study the benefits of a particular intervention, it’s rarely clear how the results apply to the public at large or to any specific patient. Clinical trials invariably enroll subjects who are relatively healthy, who are motivated to volunteer and will show up regularly for treatments and checkups. As a result, randomized trials “are very good for showing that a drug does what the pharmaceutical company says it does,” David Atkins, a preventive-medicine specialist at the Agency for Healthcare Research and Quality, says, “but not very good for telling you how big the benefit really is and what are the harms in typical people. Because they don’t enroll typical people.”

These limitations mean that the job of establishing the long-term and relatively rare risks of drug therapies has fallen to observational studies, as has the job of determining the risks and benefits of virtually all factors of diet and lifestyle that might be related to chronic diseases. The former has been a fruitful field of research; many side effects of drugs have been discovered by these observational studies. The latter is the primary point of contention.

While the tools of epidemiology — comparisons of populations with and without a disease — have proved effective over the centuries in establishing that a disease like cholera is caused by contaminated water, as the British physician John Snow demonstrated in the 1850s, it’s a much more complicated endeavor when those same tools are employed to elucidate the more subtle causes of chronic disease.

And even the success stories taught in epidemiology classes to demonstrate the historical richness and potential of the field — that pellagra, a disease that can lead to dementia and death, is caused by a nutrient-deficient diet, for instance, as Joseph Goldberger demonstrated in the 1910s — are only known to be successes because the initial hypotheses were subjected to rigorous tests and happened to survive them. Goldberger tested the competing hypothesis, which posited that the disease was caused by an infectious agent, by holding what he called “filth parties,” injecting himself and seven volunteers, his wife among them, with the blood of pellagra victims. They remained healthy, thus doing a compelling, if somewhat revolting, job of refuting the alternative hypothesis.

Smoking and lung cancer is the emblematic success story of chronic-disease epidemiology. But lung cancer was a rare disease before cigarettes became widespread, and the association between smoking and lung cancer was striking: heavy smokers had 2,000 to 3,000 percent the risk of those who had never smoked. This made smoking a “turkey shoot,” says Greenland of U.C.L.A., compared with the associations epidemiologists have struggled with ever since, which fall into the tens of a percent range. The good news is that such small associations, even if causal, can be considered relatively meaningless for a single individual. If a 50-year-old woman with a small risk of breast cancer takes H.R.T. and increases her risk by 30 percent, it remains a small risk.

The compelling motivation for identifying these small effects is that their impact on the public health can be enormous if they’re aggregated over an entire nation: if tens of millions of women decrease their breast cancer risk by 30 percent, tens of thousands of such cancers will be prevented each year. In fact, between 2002 and 2004, breast cancer incidence in the United States dropped by 12 percent, an effect that may have been caused by the coincident decline in the use of H.R.T. (And it may not have been. The coincident reduction in breast cancer incidence and H.R.T. use is only an association.)

Saving tens of thousands of lives each year constitutes a powerful reason to lower the standard of evidence needed to suggest a cause-and-effect relationship — to take a leap of faith. This is the crux of the issue. From a scientific perspective, epidemiologic studies may be incapable of distinguishing a small effect from no effect at all, and so caution dictates that the scientist refrain from making any claims in that situation. From the public-health perspective, a small effect can be a very dangerous or beneficial thing, at least when aggregated over an entire nation, and so caution dictates that action be taken, even if that small effect might not be real. Hence the public-health logic that it’s better to err on the side of prudence even if it means persuading us all to engage in an activity, eat a food or take a pill that does nothing for us and ignoring, for the moment, the possibility that such an action could have unforeseen harmful consequences. As Greenland says, “The combination of data, statistical methodology and motivation seems a potent anesthetic for skepticism.”

Healthy-User Bias

Some of the most fascinating research in observational epidemiology is now aimed at understanding the phenomenon of biased healthy users in all its insidious subtlety. Only then can epidemiologists learn how to filter out the effect of this healthy-user bias from what might otherwise appear in their studies to be real causal relationships. One complication is that it encompasses a host of different and complex issues, many or most of which might be impossible to quantify. As Jerry Avorn of Harvard puts it, the effect of healthy-user bias has the potential for “big mischief” throughout these large epidemiologic studies.

At its simplest, the problem is that people who faithfully engage in activities that are good for them — taking a drug as prescribed, for instance, or eating what they believe is a healthy diet — are fundamentally different from those who don’t. One thing epidemiologists have established with certainty, for example, is that women who take H.R.T. differ from those who don’t in many ways, virtually all of which associate with lower heart-disease risk: they’re thinner; they have fewer risk factors for heart disease to begin with; they tend to be more educated and wealthier; to exercise more; and to be generally more health conscious.

Considering all these factors, is it possible to isolate one factor — hormone-replacement therapy — as the legitimate cause of the small association observed or even part of it? In one large population studied by Elizabeth Barrett-Connor, an epidemiologist at the University of California, San Diego, having gone to college was associated with a 50 percent lower risk of heart disease. So if women who take H.R.T. tend to be more educated than women who don’t, this confounds the association between hormone therapy and heart disease. It can give the appearance of cause and effect where none exists.

Another thing that epidemiologic studies have established convincingly is that wealth associates with less heart disease and better health, at least in developed countries. The studies have been unable to establish why this is so, but this, too, is part of the healthy-user problem and a possible confounder of the hormone-therapy story and many of the other associations these epidemiologists try to study. George Davey Smith, who began his career studying how socioeconomic status associates with health, says one thing this research teaches is that misfortunes “cluster” together. Poverty is a misfortune, and the poor are less educated than the wealthy; they smoke more and weigh more; they’re more likely to have hypertension and other heart-disease risk factors, to eat what’s affordable rather than what the experts tell them is healthful, to have poor medical care and to live in environments with more pollutants, noise and stress. Ideally, epidemiologists will carefully measure the wealth and education of their subjects and then use statistical methods to adjust for the effect of these influences — multiple regression analysis, for instance, as one such method is called — but, as Avorn says, it “doesn’t always work as well as we’d like it to.”

Overall, we have much to learn on how to improve scientific studies and epidemiology to aid (and not hinder) the progression of human health. It is clear that any technological and scientific advances we have made in the last century have correlated with a global decline in human health. Perhaps this may be the most important epidemiological study for us all.