Melanie Rosenberg is interested in Big Data. When she started her career with a statistical consulting firm, she discovered that the modeling techniques she used for litigation clients translated easily to health. After whetting her appetite with a research project on diet and obesity, she wanted to pursue her growing interest in chronic disease prevention.
“Statistics is hard in health because of multiple risk factors and small effect sizes,” says Melanie, “but there is a lot of room to develop new methods.” For example, computers can scan great quantities of raw data from multiple sources, such as medical records, genomic sequences, and health surveys, to detect patterns and learn more about the underlying causes of disease. With an abundance of electronic medical data being stockpiled, biostatisticians are racing to develop new tools to interpret it.