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questions that has arisen in the decades since the Belmont Report is whether the standard approach to conducting clinical trials really does minimize risk to patients. In a conventional clinical trial, patients are split into groups, and each group is assigned to receive a different treatment for the duration of the study. (Only in exceptional cases does a trial get stopped early.) This procedure focuses on decisively resolving the question of which treatment is better, rather than on providing the best treatment to each patient in the trial itself. In this way it operates exactly like a website’s A/B test, with a certain fraction of people receiving an experience during the experiment that will eventually be proven inferior. But doctors, like tech companies, are gaining some information about which option is better while the trial proceeds—information that could be used to improve outcomes not only for future patients beyond the trial, but for the patients currently in it.

      Millions of dollars are at stake in experiments to find the optimal configuration of a website, but in clinical trials, experimenting to find optimal treatments has direct life-or-death consequences. And a growing community of doctors and statisticians think that we’re doing it wrong: that we should be treating the selection of treatments as a multi-armed bandit problem, and trying to get the better treatments to people even while an experiment is in progress.

      In 1969, Marvin Zelen, a biostatistician who spent most of his career at Harvard, proposed conducting “adaptive” trials. One of the ideas he suggested was a randomized “play the winner” algorithm—a version of Win-Stay, Lose-Shift, in which the chance of using a given treatment is increased by each win and decreased by each loss. In Zelen’s procedure, you start with a hat that contains one ball for each of the two treatment options being studied. The treatment for the first patient is selected by drawing a ball at random from the hat (the ball is put back afterward). If the chosen treatment is a success, you put another ball for that treatment into the hat—now you have three balls, two of which are for the successful treatment. If it fails, then you put another ball for the other treatment into the hat, making it more likely you’ll choose the alternative.

      Zelen’s algorithm was first used in a clinical trial sixteen years later, for a study of extracorporeal membrane oxygenation, or “ECMO”—an audacious approach to treating respiratory failure in infants. Developed in the 1970s by Robert Bartlett of the University of Michigan, ECMO takes blood that’s heading for the lungs and routes it instead out of the body, where it is oxygenated by a machine and returned to the heart. It is a drastic measure, with risks of its own (including the possibility of embolism), but it offered a possible approach in situations where no other options remained. In 1975 ECMO saved the life of a newborn girl in Orange County, California, for whom even a ventilator was not providing enough oxygen. That girl has now celebrated her fortieth birthday and is married with children of her own. But in its early days the ECMO technology and procedure were considered highly experimental, and early studies in adults showed no benefit compared to conventional treatments.

      From 1982 to 1984, Bartlett and his colleagues at the University of Michigan performed a study on newborns with respiratory failure. The team was clear that they wanted to address, as they put it, “the ethical issue of withholding an unproven but potentially lifesaving treatment,” and were “reluctant to withhold a lifesaving treatment from alternate patients simply to meet conventional random assignment technique.” Hence they turned to Zelen’s algorithm. The strategy resulted in one infant being assigned the “conventional” treatment and dying, and eleven infants in a row being assigned the experimental ECMO treatment, all of them surviving. Between April and November of 1984, after the end of the official study, ten additional infants met the criteria for ECMO treatment. Eight were treated with ECMO, and all eight survived. Two were treated conventionally, and both died.

      These are eye-catching numbers, yet shortly after the University of Michigan study on ECMO was completed it became mired in controversy. Having so few patients in a trial receive the conventional treatment deviated significantly from standard methodology, and the procedure itself was highly invasive and potentially risky. After the publication of the paper, Jim Ware, professor of biostatistics at the Harvard School of Public Health, and his medical colleagues examined the data carefully and concluded that they “did not justify routine use of ECMO without further study.” So Ware and his colleagues designed a second clinical trial, still trying to balance the acquisition of knowledge with the effective treatment of patients but using a less radical design. They would randomly assign patients to either ECMO or the conventional treatment until a prespecified number of deaths was observed in one of the groups. Then they would switch all the patients in the study to the more effective treatment of the two.

      In the first phase of Ware’s study, four of ten infants receiving conventional treatment died, and all nine of nine infants receiving ECMO survived. The four deaths were enough to trigger a transition to the second phase, where all twenty patients were treated with ECMO and nineteen survived. Ware and colleagues were convinced, concluding that “it is difficult to defend further randomization ethically.”

      But some had already concluded this before the Ware study, and were vocal about it. The critics included Don Berry, one of the world’s leading experts on multi-armed bandits. In a comment that was published alongside the Ware study in Statistical Science, Berry wrote that “randomizing patients to non-ECMO therapy as in the Ware study was unethical.… In my view, the Ware study should not have been conducted.”

      And yet even the Ware study was not conclusive for all in the medical community. In the 1990s yet another study on ECMO was conducted, enrolling nearly two hundred infants in the United Kingdom. Instead of using adaptive algorithms, this study followed the traditional methods, splitting the infants randomly into two equal groups. The researchers justified the experiment by saying that ECMO’s usefulness “is controversial because of varying interpretation of the available evidence.” As it turned out, the difference between the treatments wasn’t as pronounced in the United Kingdom as it had been in the two American studies, but the results were nonetheless declared “in accord with the earlier preliminary findings that a policy of ECMO support reduces the risk of death.” The cost of that knowledge? Twenty-four more infants died in the “conventional” group than in the group receiving ECMO treatment.

      The widespread difficulty with accepting results from adaptive clinical trials might seem incomprehensible. But consider that part of what the advent of statistics did for medicine, at the start of the twentieth century, was to transform it from a field in which doctors had to persuade each other in ad hoc ways about every new treatment into one where they had clear guidelines about what sorts of evidence were and were not persuasive. Changes to accepted standard statistical practice have the potential to upset this balance, at least temporarily.

      After the controversy over ECMO, Don Berry moved from the statistics department at the University of Minnesota to the MD Anderson Cancer Center in Houston, where he has used methods developed by studying multi-armed bandits to design clinical trials for a variety of cancer treatments. While he remains one of the more vocal critics of randomized clinical trials, he is by no means the only one. In recent years, the ideas he’s been fighting for are finally beginning to come into the mainstream. In 2010 and 2015, the FDA released a pair of draft “guidance” documents on “Adaptive Design” clinical ­trials for drugs and medical devices, which suggests—despite a long history of sticking to an option they trust—that they might at last be willing to explore alternatives.

      The Restless World

      Once you become familiar with them, it’s easy to see multi-armed bandits just about everywhere we turn. It’s rare that we make an isolated decision, where the outcome doesn’t provide us with any information that we’ll use to make other decisions in the future. So it’s natural to ask, as we did with optimal stopping, how well people generally tend to solve these problems—a question that has been extensively explored in the laboratory by psychologists and behavioral economists.

      In general, it seems that people tend to over-explore—to favor the new disproportionately over the best. In a simple demonstration of this phenomenon, published in 1966, Amos Tversky and Ward Edwards conducted experiments where people were shown a box with two lights on it and told that each light would turn on a fixed (but unknown) percentage of the time. They were then given 1,000 opportunities

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