Lauren Forrow is a senior statistician specializing in causal inference, with expertise in comparison group selection and Bayesian hierarchical modeling. Forrow has been involved in methods development in both areas, helping to develop new matching and weighting techniques and extending cutting-edge Bayesian approaches to causal inference.
Forrow has led the tasks to select comparison groups for several large high-profile health care demonstrations, including Comprehensive Primary Care Plus, the Health Care Innovation Awards Round 2, the Transforming Clinical Practice Initiative, the Primary Care First evaluation, and the evaluation of the Medicare Care Choices Model. She also led the Bayesian impact analysis of many of these evaluations: the Health Care Innovation Awards Round 2, the Medicaid study for the Transforming Clinical Practice Initiative, and the evaluation of the Medicare Care Choices Model. She currently serves as task lead for the Bayesian analyses conducted for the evaluation of the Primary Care First model.
Forrow is a member of the American Statistical Society. Her work has appeared in the Journal of the Royal Statistical Society, The American Statistician, and Health Services Research, among other publications. She holds a master’s degree in statistics from Harvard University.
This Health Affairs article presents differences in outcomes between Medicare Care Choices Model enrollees that received treatment for terminal conditions along with supportive and palliative care and matched comparison beneficiaries who received usual services covered by original Medicare.
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