Here’s a thought experiment for you:
If someone told you you had to drink just one kind of alcoholic beverage for the rest of your life, and you wanted that life to be long and healthy, what would you pick? Wine, right? After all, you’ve probably heard about the scientific studies showing that drinking wine is associated with better health in general, and a longer life span in particular. Give jocks their beer and lushes their hard liquor; the drink of robust, long-lived people is wine.
But you have probably not heard about another study, released during the media dead zone just after Christmas last year, that questioned wine’s reputed health effects. Researchers at Stanford University and the University of Texas at Austin examined a group of Americans aged 55 to 65 and compared their drinking habits with how they fared over the course of 20 years. The scientists found that moderate drinkers lived longer than abstainers, and that wine drinkers did indeed live longer on average than people who consumed other kinds of alcohol. But they also found that wine drinkers were less likely to smoke, to be male, and to be sedentary; all of these are factors associated with dying earlier.
The Stanford-Texas team concluded that drinking wine might be an indicator of a healthy lifestyle rather than the cause of that good health. If so, wine is the drink of the healthy, all right—the already healthy.
That finding highlights what is arguably science’s greatest enemy, the confounder. Science is at heart a reductionist process: Take a complicated system, identify various factors that affect the system, and measure the effect of each factor one at a time. Confounders are devilish hidden connections that make it more difficult to isolate the factors you want to measure, like the fact that wine drinkers tend also to be nonsmokers.
Researchers are continually trying to root out confounders and account for them in their data. Their most powerful tool in this job is the randomized controlled trial, a type of experiment in which researchers separate participants into two or more groups and subject some of them to the intervention to be studied, like a new drug or surgical procedure.
New medical interventions must be proven safe and effective in a randomized controlled trial before the Food and Drug Administration (FDA) will approve their use. Though seen as the gold standard of medical research, such studies—even ones involving thousands of participants—may be too small to ferret out rare risk factors or side effects. And when it comes to claims about food, randomized trials may never be conducted at all. Few Bud or bourbon drinkers will switch to burgundy for 20 years just for the sake of research and a bit of cash.
In 2009 epidemiologists at Harvard
Medical School developed a way for scientists to account for the invisible connections that were confounding their studies. The new approach uses an algorithm that automatically identifies and adjusts for confounders as well as or better than the most knowledgeable scientist can, says Jeremy Rassen, one of the algorithm’s creators.
Wine may be
an indicator of a
rather than a cause
of good health. If so,
it is the drink of
Called the high-dimensional propensity score algorithm (hd-PS), it is a tool for improving not randomized clinical trials but broader observational studies, in which researchers watch a large pool of participants and look for correlations—like the fact that wine drinkers live longer than other drinkers. Observational studies are cheaper and easier than clinical trials. Unfortunately, the data they yield are rife with confounder problems, but researchers can improve the data by adjusting for suspected confounders and removing the bias they introduce. In the recent observational study on wine and longevity, for instance, after the researchers accounted for smoking, gender, and activity level, they found that beer and hard liquor were just as life-extending as wine.
And this is where hd-PS shines. While a shrewd researcher with decades of experience might adjust for a few dozen confounders, Rassen’s algorithm can easily identify 500 of them. To use hd-PS, a researcher downloads the program from the Harvard site, connects it to one of the data software packages widely used in epidemiology, and imports to the system a wide range of health information on each study subject, ranging from basics like blood pressure and age to smaller, more esoteric factors like whether the individual saw a doctor in the past six months.
Then hd-PS ingests all this information—“it’s a data-hungry algorithm,” Rassen says—and churns away on some heavy number-crunching. At the heart of the crunching is a process called propensity score matching. The algorithm sorts through all the variables in the data and isolates ones that seem to be risk factors for a particular health problem. It combines those risk factors into one summary score, and compares two groups that have identical summary scores but also one key difference. Beer-drinkers and wine-drinkers with the same summary scores, for instance, differ in their preferred drink but are otherwise at exactly the same risk level. The computer by itself accomplishes the key task of isolating each variable and measuring its effect.