Maya Petersen is an Assistant Professor of Biostatistics and Epidemiology at the University of California, Berkeley. She is currently on leave from her faculty appointment to complete an M.D. at the University of California, San Francisco. Maya received a B.A. in Human Biology from Stanford University, and a M.S. in Health and Medial Science from the Berkeley School of Public Health. She received a Ph.D. in Biostatistics from Berkeley, where her doctoral work was funded by the Howard Hughes Medical Institute and was honored by the Evelyn Fix prize. Maya’s research interests include the treatment of HIV resistant to antiretroviral drugs, the use of antiretroviral therapy in the developing world, and the use of machine learning methods to estimate the effects of viral mutations. Methodologically, she is interested in the application of causal inference methods to observational clinical datasets, the use of statistics to improve clinical trial design, and the development of methods to estimate individualized treatment rules (also known as dynamic treatment regimes). Maya has a strong interest in the interface between biostatistics, epidemiology, and clinical medicine, including the communication of new statistical methods to non-statistical audiences, and the application of advances in biological and clinical understanding of disease to drive the development of new statistical methodologies.
Articles
History-Adjusted Marginal Structural Models to Estimate Time-Varying Effect Modification (with Steven Deeks, Jefferey Martin, and Mark van der Laan), American Journal of Epidemiology (2007)
Hospital-based surveillance of meningococcal meningitis in Salvador, Brazil (with Soraia Cordeiro, Alan Neves, Cassio Ribeiro, Edilane Gouveira, Guilherme Ribeiro, Tatiana Lobo, Joice Reis, Katia Salgado, Mittermeyer Reis, and Albert Ko), Transactions of the Royal Society of Tropical Medicine and Hygiene (2007)
Individualized treatment rules: Generating candidate clinical trials (with Steven G. Deeks and Mark J. van der Laan), Statistics in Medicine (2007)
Pillbox Organizers are Associated with Improved Adherence to HIV Antiretroviral Therapy and Viral Suppression: A Marginal Structural Model Analysis. (with Yue Wang, Mark van der Laan, David Guzman, Elise Riley, and David Bangsberg), Clinical Infectious Diseases (2007)
Cross-validated Bagged Learning (with Annette Molinaro, Sandra E. Sinisi, and Mark J. van der Laan), Journal of Multivariate Analysis (2007)
Contributions to Books
Identifying important explanatory variables for time-varying outcomes. (with Oliver Bembom and Mark J. van der Laan), Fundamentals of Data Mining in Genomics and Proteomics (2006)
The healthcare system and HIV epidemic in Brazil. (with Claudia Travassos, Francisco I. Bastos, Mariana Hacker, Eduard J. Beck, and Jose Carvalho de Noronha), The HIV Pandemic: local and global implications. (2005)
Management of HIV/AIDS: The Brazilian experience. (with Francisco I. Bastos, Diane Kerrigan, and Marie-Claude Bioly), Antiretroviral treatment: Experiences and challenges. [In French] (2004)
Selected Technical Reports
Biomarker Discovery Using Targeted Maximum Likelihood Estimation: Application to the Treatment of Antiretroviral Resistant HIV Infection (with Oliver Bembom, Maya L. Petersen , Soo-Yon Rhee , W. Jeffrey Fessel , Sandra E. Sinisi, Robert W. Shafer, and Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2007)
Diagnosing Bias in the Inverse Probability of Treatment Weighted Estimator Resulting from Violation of Experimental Treatment Assignment (with Yue Wang, David Bangsberg, and Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2006)
Direct Effect Models (with Mark J. van der Laan), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)