B.S., University of Vermont, 2008; MPhil, University of Cambridge, UK, 2009; PhD, University of California, Berkeley, 2015; Post-Doctoral Fellow, Harvard School of Public Health, 2015-2017
Area(s) of Specialization:
Causal inference, Machine Learning, Cluster randomized and pragmatic trials, Epidemiologic methods, Differential measurement, missingness, and censoring
I am methodologist with substantive interests in global health, community-based participatory research, and social determinants of health. My areas of expertise are Causal Inference and Machine Learning. These disciplines are integral to developing, evaluating, and implementing data-driven solutions in Public Health and Medicine. My research has focused on challenges that arise when making causal inferences with clustered and longitudinal data. I am is also interested the design and analysis of pragmatic trials and observational studies, which are both subject to complex measurement, missingness, and dependence.
I am the Primary Statistician for three cluster randomized trials in East Africa: the SEARCH study to prevent HIV and improve community health, the SATURN study to improve care outcomes among HIV+ youth, and the SPIRIT study to prevent TB. My work is supported by the National Institutes of Health (NIH) and has been recognized by ASA’s Causality in Statistics Education Award and the Gertrude M. Cox Scholarship. Overall, my research is informed by cross-disciplinary real-world problems and aims to ensure methodological advances in Academia translate into real-world impact.
L. Balzer, D. Havlir, M. Kamya, G. Chamie, et al. Machine learning to identify persons at high-risk of HIV acquisition in rural Kenya and Uganda. Clinical Infectious Diseases, In Press, 2019.
D.V. Havlir, L.B. Balzer, E. Charlebois, T.D. Clark, et al. Trial of HIV test and treat via a community health model in rural Africa. New England Journal of Medicine, 381:219–229, 2019
L.B. Balzer, W. Zheng, M.J. van der Laan, M.L. Petersen, and the SEARCH Collaboration. A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure. Statistical Methods in Medical Research, OnlineFirst, 2018
M. Petersen, L. Balzer, D. Kwarsiima, N. Sang, et al. Association of implementation of a universal testing and treatment intervention with HIV diagnosis, receipt of antiretroviral therapy, and viral suppression among adults in East Africa. JAMA, 317(21):2196–2206, 2017.
L.B. Balzer. “All generalizations are dangerous, even this one.” - Alexandre Dumas [Commentary]. Epidemiology, 28(4):562–566, 2017.