Laura is a senior fellow in the Statistics & Data Science department whose work combines advanced analytical methods with big data to answer policy-relevant questions. Her expertise spans several critical areas, including quantifying variation and causal inference. She has developed statistical tools to reveal patterns of variation, identify subgroups, and quantify tradeoffs in health care measures. These techniques can point decision-makers towards targets for intervention in health care systems. Her current research in this area extends network analysis methods to account for complex dependence structures in models for oncology care.

Laura has also expanded and sharpened methods for quasi-experimental causal inference. These techniques can determine “what works” when randomization is not feasible and are therefore popular in program and policy evaluations across the social sciences. Her current research in this area is supported by the Agency for Healthcare Research and Quality (AHRQ) to develop new difference-in-differences methods tailored for health equity impact evaluations.

In addition to her methodological innovations, Laura has contributed significantly to health policy and health services research. Her applied research spans topics from price transparency and bundled payments to hospitalist models and prenatal care. Currently, she provides statistical leadership for the International Health Systems Research Collaborative, a network of teams from eleven countries that studies variation in hospital care. She also leads the Data and Methods Cores of two NIH-funded Program Project grants, one on patient care and outcomes in a changing Medicare program, and the other on integration in the cancer care delivery system. Prior to joining NORC, Laura was an associate professor of health care policy (biostatistics) at Harvard Medical School.