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 a methodologist with substantive interests in global health, community-based participatory research, and social determinants of health. My particular areas of expertise are Causal Inference and Machine Learning. These disciplines are integral to developing, evaluating, and implementing data-driven solutions in Public Health. My research has focused on challenges that arise when making causal inferences with clustered and longitudinal data. I am 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 two cluster randomized trials: the SEARCH study for HIV prevention in East Africa and the SPIRIT study for TB prevention in Uganda. Overall, my research is informed by cross-disciplinary real-world problems and aims to ensure methodological advances in Academia translate into real-world impact.
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.
L. Balzer, P. Staples, J. Onnela, and V. DeGruttola. Using network-based simulations to evaluate the effect of adding targeted PrEP to an ongoing treatment-as-prevention trial. Clinical Trials, Jan:1–10, 2017.
L. Balzer, M. van der Laan, M. Petersen, and the SEARCH Collaboration. Adaptive pre-specification in randomized trials with and without pair-matching. Statistics in Medicine, 35(25):4528–4545, 2016.
L. Balzer, M. Petersen, M.J. van der Laan, and the SEARCH Collaboration. Targeted estimation and inference of the sample average treatment effect in trials with and without pair-matching. Statistics in Medicine, 35(21):3717–3732, 2016.