Rocky Aikens

Rachael “Rocky” Aikens is an expert in experimental and quasi-experimental design with over six years’ experience in applied statistics in clinical and policy research. She specializes in developing methods to design more robust and powerful observational studies with a focus on approaches for comparison group selection. She has held lead statistical roles on multiple large Medicare model evaluations for the Center for Medicare and Medicaid Innovation.

At Mathematica, Dr. Aikens’s work has centered on comparison group selection, causal inference, Bayesian statistics, and subgroup analyses. She was the lead statistician for selecting the final comparison group for the evaluation of the Primary Care First Model and now leads the regression team for assessing the causal impacts of the model on health care costs and quality. 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 primary care, clinical informatics, behavioral health, and Medicare and Medicaid policy.

Dr. 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, JAMA Network Open, and the Journal of the American Medical Informatics Association, among others. She holds a Ph.D. in biomedical informatics from Stanford University.