StATS: I wasn't making up data, I was imputing! (October 25, 2006)
The New York Times has an informative summary of a recent research scandal involving a prominent researcher at the University of Vermont, Eric Poehlman.
The Poehlman scandal represents perhaps the biggest cases of research fraud in recent history.
He presented fraudulent data in lectures and in published papers, and he used this data to obtain millions of dollars in federal grants from the National Institutes of Health — a crime subject to as many as five years in federal prison. Poehlman’s admission of guilt came after more than five years during which he denied the charges against him, lied under oath and tried to discredit his accusers. By the time Poehlman came clean, his case had grown into one of the most expansive cases of scientific fraud in U.S. history.
The first person to speak up about the possibility of fraud in Poehlman's work was one of his research assistants, Walter DeNino.
The fall that DeNino returned to the lab, Poehlman was looking into how fat levels in the blood change with age. DeNino’s task was to compare the levels of lipids, or fats, in two sets of blood samples taken several years apart from a large group of patients. As the patients aged, Poehlman expected, the data would show an increase in low-density lipoprotein (LDL), which deposits cholesterol in arteries, and a decrease in high-density lipoprotein (HDL), which carries it to the liver, where it can be broken down. Poehlman’s hypothesis was not controversial; the idea that lipid levels worsen with age was supported by decades of circumstantial evidence. Poehlman expected to contribute to this body of work by demonstrating the change unequivocally in a clinical study of actual patients over time. But when DeNino ran his first analysis, the data did not support the premise.
When Poehlman saw the unexpected results, he took the electronic file home with him. The following week, Poehlman returned the database to DeNino, explained that he had corrected some mistaken entries and asked DeNino to re-run the statistical analysis. Now the trend was clear: HDL appeared to decrease markedly over time, while LDL increased, exactly as they had hypothesized.
Although DeNino trusted his boss implicitly, the change was too great to be explained by a handful of improperly entered numbers, which was all Poehlman claimed to have fixed. DeNino pulled up the original figures and compared them with the ones Poehlman had just given him. In the initial spreadsheet, many patients showed an increase in HDL from the first visit to the second. In the revised sheet, all patients showed a decrease. Astonished, DeNino read through the data again. Sure enough, the only numbers that hadn’t been changed were the ones that supported his hypothesis.
When Poehlman brushed DeNino's concerns aside, so DeNino started asking around and other graduate students and postdocs had similar concerns. He got some cautionary advice from a former postdoctoral fellow
Being associated with either falsified data or a frivolous allegation against a scientist as prominent as Poehlman could end DeNino’s career before it even began.
and a faculty member who shared lab space with Poehlman advised
If you’re going to do something, make sure you really have the evidence.
So DeNino started looking for the evidence.
DeNino spent the next several evenings combing through hundreds of patients’ records in the lab and university hospital, trying to verify the data contained in Poehlman’s spreadsheets. Each night was worse than the one before. He discovered not only reversed data points, but also figures for measurements that had never been taken and even patients who appeared not to exist at all.
DeNino presented his evidence to the university counsel and the response of Poehlman to his department chair, Burton Sobel, was rather startling.
The accused scientist gave him the impression that nothing was wrong and seemed mostly annoyed by all the fuss. In his written response to the allegations, Poehlman suggested that the data had gotten out of hand, accumulating numerous errors because of handling by multiple technicians and postdocs over the years. “I found that noncredible, really, for an investigator of Eric’s experience,” Sobel later told the investigative panel. “There had to be a backup copy that was pure,” Sobel reasoned before the panel. “You would not have postdocs and lab techs in charge of discrepant data sets.” But Poehlman told Sobel that there was no master copy.
At the formal hearing, Poehlman had a different defense.
First, he attributed his mistakes to his own self-proclaimed ineptitude with Excel files. Then, when pressed on how fictitious numbers found their way into the spreadsheet he’d given DeNino, Poehlman laid out his most elaborate explanation yet. He had imputed data — that is, he had derived predicted values for measurements using a complicated statistical model. His intention, he said, was to look at hypothetical outcomes that he would later compare to the actual results. He insisted that he never meant for DeNino to analyze the imputed values and had given him the spreadsheet by mistake.
The New York Times article points out how pathetic this attempted explanation was.
Although data can be imputed legitimately in some disciplines, it is generally frowned upon in clinical research, and this explanation came across as hollow and suspicious, especially since Poehlman appeared to have no idea how imputation was done.
A large portion of the article examines how research fraud can occur in a system that is supposed to be self-correcting.
First, the people who are mostly likely to notice fraud are junior investigators who are subordinate to their research mentor. It's psychologically and emotionally difficult to confront someone who has devoted time to your professional development. Even when an investigator is emotionally willing to confront their mentor, they have their career concerns to worry about.
The principal investigator in a lab has the power to jump-start careers. By writing papers with graduate students and postdocs and using connections to help obtain fellowships and appointments, senior scientists can help their lab workers secure coveted tenure-track jobs. They can also do damage by withholding this support.
Every university will have a system in place to investigate claims of fraud. But there are problems here as well.
All universities that receive public money to conduct research are required to have an integrity officer who ensures compliance with federal guidelines. But policing its scientists can be a heavy burden for a university. “It’s your own faculty, and there’s this idea of supporting and nurturing them,” says Ellen Hyman-Browne, a research-compliance officer at the Children’s Hospital of Philadelphia, a teaching hospital. Moreover, investigations cost time and money, and no institution wants to discover something that could cast a shadow on its reputation.
“There are conflicting influences on a university where they are the co-grantor and responsible to other investigators,” says Stephen Kelly, the Justice Department attorney who prosecuted Poehlman. “For the system to work, the university has to be very ethical.”
Poehlman himself was careful and chose areas where fraud would be especially difficult to detect. He specialized in presenting longitudinal data, data that is very expensive to replaicate. He also presented research results that confirmed what most researchers had suspected, rather than results that would undermine existing theories of nutrition.
At his sentencing, Poehlman was sentenced to one year and one day in federal prison, making him the first researcher to serve time in jail for research fraud.
“When scientists use their skill and their intelligence and their sophistication and their position of trust to do something which puts people at risk, that is extraordinarily serious,” the judge said. “In one way, this is a final lesson that you are offering.”
I'm going to submit this material to the Chance News wiki site.
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