April 15, 2015
(Courtesy UMass Amherst News Office)
As she explains, “significant federal investment has made available huge repositories of behavioral, genotypic and phenotypic data collected from large, prospective studies such as the Women’s Health Initiative.” Her interdisciplinary team proposes to develop and apply new statistical methods to mine these rich and rapidly growing databases to investigate the causes of complex disorders such as diabetes and cardiovascular disease, which involve a combination of genetic, environmental and lifestyle factors. Because such studies enroll several hundreds of thousands of subjects who are prospectively followed for long periods, several measures are built in to their design to reduce costs.
One such feature is the use of periodic self-report of behavior, for example, rather than direct measurement. “Although cost-effective, self-reports are prone to error,” says Balasubramanian. “By appropriately accounting for the error in self-reported outcomes, we focus on development of statistical tools for study design, causal inference in non-randomized settings as well as methods for mining high-dimensional datasets such as in high-throughput metabolomic studies.”
Balasubramanian joined the faculty in 2008. She served previously as director of biostatistics at a biotechnology start-up, BG Medicine Inc., Waltham, where she was involved in the design and analysis of several biomarker discovery studies incorporating high throughput ‘omics’ technologies.
Balasubramanian’s collaborators on this project are at UMass Medical School, Georgetown University, Harvard School of Public Health, Brigham and Women’s Hospital and the University of Washington.