Rocky Aikens

Rachael “Rocky” Aikens is an expert in experimental and quasi-experimental techniques for policy evaluation. She specializes in developing methods to design more robust and powerful observational studies with a focus on approaches for comparison group selection. Her recent work focuses on measurement error and its implications for studying health inequities.

At Mathematica, Aikens’s work has centered on comparison group selection and analyses of health inequities. She helped design the comparison group for the evaluation of the Primary Care First Model and conducted its analysis of baseline health inequities, including an assessment of how measurement error in imputed race and ethnicity information can impact health disparities analyses. Before Mathematica, her research centered on using risk score information to design stronger observational studies through matching, stratification, and visual diagnostics. She has collaborated across wide-ranging topic areas, including pediatrics, clinical decision support, statistical genetics, and behavioral health.

Aikens has been recognized with multiple awards for her outstanding scientific communication and noted for her ability to present technical topics in accessible ways to broad audiences. Her research is published in The American Statistician, Statistics in Medicine, the Journal of the American Medical Association Network Open, and the Journal of the American Medical Informatics Association, among others. She holds a Ph.D. in biomedical informatics from Stanford University.