In October, 2003, the scientist Lewis Sheiner sent an e-mail to Nick Holford, a former student, recalling how their field of pharmacometrics had begun. Within a few months, Sheiner was dead. The following year, Holford quoted a brief passage of the correspondence in a memorial tribute. When I reached out to him this summer for the rest of the exchange, he searched his archives and wrote back: “I cannot find the original email from Lewis that I cited.” That left only the brief scene he had already committed to print.
And it goes like this. A seminar room at the University of California, San Francisco, sometime around 1970. A young physician is up front, presenting his work on drug dosing, when an even younger economist starts interrupting from the audience — takes his math apart piece by piece, then rebuilds the field on the spot.
I spent several weeks this summer tracking the people and the institutions that grew out of that afternoon, guided by oral histories of retired clinical pharmacologists, veterans of the FDA, and a programmer who spent her career building the software that resulted from that chance encounter. The economist in the room was Barr Rosenberg. The doctor was Lewis “Lou” Sheiner. From their collision emerged pharmacometrics: the statistical scaffolding for how drugs move through populations, how doses of medicine are chosen, and how medicine attempts to learn from the sparse data of human bodies.
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Barr Rosenberg was a forecaster of financial risk, and he was quite good at it. Born in Berkeley in 1942, he enrolled at the University of California at sixteen, secured a master’s degree from the London School of Economics by twenty-two, and completed a Harvard doctorate in economics at twenty-five. His dissertation carried a title only a statistician could truly appreciate: Regression with Stochastically Varying Parameters. The topic was a happy accident. He had originally intended to analyze capital markets using a pristine new stock-price database in Chicago, but when the data delivery stalled, he abandoned capital markets for pure theory, focusing entirely on how to model systems with underlying variables that continuously shifted over time.
That theoretical detour proved immensely valuable. The late 1960s and 1970s marked the dawn of quantitative finance, an era when it suddenly felt possible to quantify the invisible risks lurking inside a stock portfolio. In 1974, Rosenberg launched a consulting firm, BARRA. The reigning method scored a stock’s risk with a single number: how much it moved with the market. Rosenberg’s models pulled that risk apart across many features of the company itself. Suddenly, an oil company’s risk could ride on the price of crude and a bank’s on interest rates, a homebuilder’s to mortgage rates. The shift from one factor to many became, and remains, the working grammar of quantitative finance. It made BARRA’s tools standard equipment for institutional investors.
As Sheiner’s lost email tells it, Rosenberg’s foray into medicine began around 1967, when his mother’s heart began to fail. To keep her heart pumping, her doctors continually increased her dose of a heart medication called digoxin. First described in 1785 by the English physician William Withering, digoxin is a potent compound derived from the bell-shaped blooms of the foxglove plant. It is also a notoriously temperamental drug. It works by jamming microscopic sodium-potassium pumps in the membranes of heart muscle cells, causing calcium to pool inside. That extra calcium acts like a chemical whip, goading a weakened, flabby heart to contract with fresh intensity.
The margin for error with digoxin is perilously thin. If you underdose it, nothing happens. Make the mistake of overdoing it, and so much calcium floods the cells that the heart’s electrical system misfires, throwing the heart into a dangerous rhythm of its own. During the 1960s, doctors had no way of knowing where a patient sat within that narrow therapeutic window, because they lacked any reliable method to measure digoxin levels in a given person’s blood. So, equipped with nothing else, a doctor could only make an educated guess, tweak the dose this way and that way, and watch how their patient responded to it. A 1971 study found that about a quarter of hospitalized patients on digoxin were being poisoned by the drug. One of them was Rosenberg’s mother — who, by fate, was on the wrong side of a physician’s guesswork: every time her doctors raised her dose, she got worse.
Frustrated by the ordeal, Rosenberg began badgering his mother’s medical team with what seemed to him an obvious question. If he could use mathematics to model the volatile swings of the stock market, why couldn’t highly trained medical professionals use similar tools to chart a drug’s course through the human body?
Rosenberg saw no real difference between the stock market and human metabolism. To him, they were just two versions of the same puzzle. Both were vast, invisible systems that doctors and economists could only guess at using sparse, noisy clues — like a closing bell price or a morning blood draw. But where medicine saw missing data as a tragedy, Rosenberg saw it as a math problem waiting to be solved.
The prevailing statistical paradigm of that era treated patients as mere copies — interchangeable units stamped from a single biological template. Doctors would identify a theoretical “average” patient, calculate the statistical mean of how much of a medication would be needed to treat them, and then administer that generalized dose to everyone on the ward. They would then watch, often helplessly, as real, idiosyncratic human beings wildly diverged from that neat mathematical average.
Rosenberg’s idea was to give every patient a biology of their own: a particular rate of clearing a drug through the kidneys, a particular volume of blood to dilute it in. It did not leave them there alone. It set each one inside a larger picture, like a single, uniquely colored dot in a Pointillist painting.
On its own, a single data point doesn’t tell doctors much, but analyzing thousands of them together reveals a clear pattern. Statisticians have a homely, almost quaint name for this trick: “borrowing strength.”
In practice, you start a new patient on a dose derived from the wider population. It’s a lot like the old habit of just grabbing the “average,” but this time, you know exactly how reliable that baseline is. Then, as that patient’s own blood levels of a drug arrive, Rosenberg’s method slides the estimate off the crowd and onto them, until it describes that person and no one else.
All Rosenberg needed was a physician willing to let him test the math.
