Cancer. It's a leading cause of death in the United States and a leading inspiration behind the lifetime research agenda of Texas A&M University Distinguished Professor of Statistics Raymond J. Carroll.

According to the American Cancer Society, about one-third of the more than 572,000 U.S. cancer deaths that occur each year will be related to poor diet, physical inactivity or obesity and, thus, could be prevented.

Carroll has dedicated many of his research efforts to doing just that, helping Americans beat their odds of contracting the nation's second deadliest disease by working to ensure the validity of the studies that help predict those odds.

Since 1991, Carroll has been analyzing the missing link between diet and cancer -- or, more specifically, why so many studies find no relationship between the two due to errors in reporting and analysis.

"Typical nutritional surveys feature questions like, 'How many calories a day do you consume?' and 'What percentage of your diet has fat in it?'" Carroll explains. "These are questions nobody can really answer."

In the quest to offset ambiguity, Carroll and his legion of protégés have made many fundamental contributions to the area of statistical expertise known as measurement error modeling -- providing reliable analyses in situations where variables and exposures are measured with error.

In Carroll's case, his primary targets are breast and colon cancer and the possible nutritional and lifestyle factors impacting both, from fat intake to smoking. His statistical weapon of choice involves nutritional epidemiology, a high-stakes field in which the name of the game is to develop questionnaires that elicit more accurate answers and, therefore, more reliable data.

Years of using statistics to quantify survey uncertainty have led Carroll and his research group to two main conclusions: Size matters. So does method.

"One of the impacts of my work has been to show that there is a true need for large samples, because it's incredibly hard to measure and quantify survey results," he says. "To be reliable, sample sizes need to be in the hundreds of thousands."

Carroll notes that survey designers typically rely on three measurement instruments to help gauge American dietary habits: food frequency questionnaires, 24-hour recalls and food diaries.

To put the inexactness of this science into proper perspective, he cites a classic food frequency questionnaire staple -- the "pizza question," in which respondents are asked how often they eat pizza, how many pieces they eat and whether or not they include meat.

"In just three questions, they try to capture your lifetime fat intake from pizza," he explains. "Then they have to convert your answers into nutrient values. There are uncertainties in every step of the process, from how people answer, to the conversions. The reason for requiring large samples is that the instruments aren't very good."

In addition to introducing error and uncertainty, food frequency questionnaires can't measure caloric intake, which Carroll says has a huge impact on obesity studies.

He considers the 24-hour recall method equally problematic, because it looks at a very small snapshot of dietary history when the long-term is more important. Moreover, it's expensive and, therefore, highly cost-prohibitive.

A better alternative to both methods is food diaries, in which respondents report actual eating habits for a week at a time on three or four different occasions throughout the year.

"Food diaries are really good instruments because most Americans would be appalled at what they report," Carroll adds. "They are a much better way of measuring, not to mention modifying."

In a recently published study of data from the Women's Health Initiative using food diaries and a sample of 30,000 women, Carroll and his colleagues found a statistically significant relationship between fat intake and breast cancer.

"I'm not a nutritional epidemiologist; I'm a hard-core statistician," he notes. "But what I've done is design a statistical method that goes into the analysis of these big data sets. Our group has come up with statistical methods and analyses unique to the problem of measuring diet."

Carroll says while the hallmark staples of a healthy diet primarily consisting of fruit, vegetables and whole grains still apply regardless of measurement device, the biggest change he's seen in his own philosophy over time is that looking at one food or nutrient at a time is exactly the wrong way to go.

"Suggestions like 'eat less saturated fat' are all well and good, but it's the entire diet that matters," Carroll says. "There is something called the Healthy Eating Index-2005 (HEI-2005) that captures adherence to the USDA Dietary Guidelines, and it is very predictive of colon cancer. I'm working on studying the distribution of the HEI across the country and between groups."

As the lead statistician looking at HEI distribution, Carroll applies his scientific expertise to determine potentially relevant breakdowns, such as the percentage of children who have poor diets or the dietary differences between smokers and non-smokers. (For the record, he says smokers generally have much worse diets than non-smokers.) In addition to providing reality checks and identifying potential red flags, he also helps ensure real-world benchmarks when it comes to defining success.

"The 2010 White House Task Force on Obesity originally aimed for the goal that every child in the U.S. would get a diet score of 80, but it's really hard to get there," Carroll says. "We did the analysis at the time and concluded there were no children in the U.S. who had an 80. They were trying to go from zero to 80 -- a massive improvement -- which was not an achievable goal. We convinced them to aim for a mean of 80 and a score of 53, which is still ambitious but within the realm of possibility."

In 2001 as part of a personal effort to expand the possibilities, Carroll came up with a novel way to train the next generation of statistical scientists, helping to shape a new genre of interdisciplinary research known as bioinformatics in the process. Backed by a $1.6 million National Cancer Institute (NCI) grant renewed last year for the third consecutive time and funded through 2016, he established the Bioinformatics, Biostatistics and Nutrition Training Program, which is housed in the Texas A&M Center for Statistical Bioinformatics that he founded in 2007. The unique post-doctoral program -- one of only a handful nation-wide in biostatistics -- seeks to build bridges between the life sciences (biology and genetics) and computational sciences (statistics) to better train future statisticians to function as independent researchers as they continue to explore links between nutrition and cancer.

"I was developing statistical methods for more interdisciplinary projects by myself by reading books on biology," Carroll recalls. "I had a wonderful student at the time named Jeff Morris who helped a lot, but essentially, I proved that a statistician could learn enough biology to be useful to a biologist."

Carroll was rewarded in 2005 for his pioneering efforts in nutritional epidemiology and biology with an NCI Method to Extend Research in Time (MERIT) Award. Less than 1 percent of all National Institutes of Health-funded investigators merit selection for the highly selective award, which includes up to 10 years of grant support. Carroll is the first winner in Texas A&M history as well as the first statistician to be chosen by the NCI since the inception of the program in 1987.

Never one to rest on his laurels, Carroll is well on his way to achieving measurable success in his next challenge -- analysis of environmental effects on health, including radiation exposure and cancer risk related to a Nevada bomb test site study.

"All of us living in the U.S. in the 1950s were exposed to radiation when the government tested bombs at sites in Nevada," he explains. "Regardless of what the weather conditions were like back then, there were a lot of deposits of radiation in the ground. Cows ate the grass; people drank the cows' milk; people developed thyroid disease."

In 2011 Carroll was invited to Ukraine to tour the Chernobyl Nuclear Power Plant, the site of the worst nuclear-power-related accident in international history. The 1986 catastrophic event is one of only two classified as a level 7 on the International Nuclear and Radiological Event Scale, and its long-term effects, such as cancers and deformities, are still being evaluated.

"I am one of several people advising them on how to analyze their data, which in itself is extremely remarkable," Carroll says. "Unlike many of the studies in the United States, they actually were able to measure people's thyroid activity right on the spot, which should yield unprecedented information.

"Statisticians always talk in terms of probability. We never make categorical statements. It's extremely difficult to say with absolute certainty that this caused you to have this problem or condition. It's a population science."

One that never gets boring for Carroll, who notes with a slight degree of amusement that he's changed research areas four times since entering a profession he loves back in the 1970s.

"Statistics is just fun and cool," he adds. "You don't actually know what the answers are going to be when you start. Plus, the skills are transferable to interesting areas."

To learn more about Carroll's research, career expertise and accomplishments, visit http://www.stat.tamu.edu/~carroll/.

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About 12 Impacts for 2012: 12 Impacts for 2012 is an ongoing series throughout 2012 highlighting the significant contributions of Texas A&M University students, faculty, staff and former students on their community, state, nation and world. To learn more about the series and see additional examples, visit http://12thman.tamu.edu/.


Watch an interview with Dr. Carroll about his research on You Tube:

Contact: Shana K. Hutchins, (979) 862-1237 or shutchins@science.tamu.edu or Dr. Raymond J. Carroll, (979) 845-3170 or carroll@stat.tamu.edu

Hutchins Shana

  • Cause and Effect

    Carroll last spring at Chernobyl in front of Reactor 4, the epicenter of the 1986 nuclear explosion, the most devastating in world history. (Note the personal radiation dosimitor attached to his shirt pocket.)

© Texas A&M University. To request use of any of our photographs for educational use or to view additional options from our archive, please contact the College of Science Communications Office.

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