Biostatisticians

Director of the Biostatistics Core
Biostatistician

Dr. Montez-Rath completed her PhD in Biostatistics from Boston University in 2008 focusing on methods for modeling interaction effects. She has been working as a biostatistician in the Division of Nephrology  More at Stanford University since 2010 where she collaborates with numerous clinical investigators to study a variety of research questions relevant to kidney disease. Her methodological interests are mainly data-driven and much of her research involves data collected from the USRDS Database, a rich data source describing all end stage renal disease patients in the United States. She is also currently a co-investigator on a PCORI-funded project entitled “The Handling of Missing Data Induced by Time-Varying Covariates in Comparative Effectiveness Research Involving HIV Patients.” Besides missing data, her statistical interests focus on methods for analyzing epidemiologic studies, analysis of survival type outcomes, analysis of correlated data and comparative effectiveness studies.

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Yuanchao Zheng, MS
Biostatistician

Yuanchao received her bachelor’s degree in Statistics from the University of Science and Technology of China in 2010 and her Master's degree in Biostatistics from Brown University in 2012.  More She then worked as a programmer and analyst at Brown University Center for Statistical Sciences, where she earned experience in cancer imaging research and data management from the National Oncology PET Registry (NOPR). In 2014, she joined the Division of Nephrology at Stanford as a biostatistician. Currently, her primary work is to collaborate clinical investigators on kidney research projects, using the United States Renal Data System (USRDS) and the National Inpatient Sample (NIS). She also works on the United States Census and the American Community Survey (ACS). Her research interests focus on statistical methods for analyzing epidemiological studies and clinical trials.

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Margaret Stedman, PhD
Biostatistician

Dr. Margaret Stedman joined the Nephrology division in 2015.  She has been collaborating on projects related to the allocation of kidney transplant using the  SRTR and STRIDE databases. Prior to coming to Stanford she completed her PhD More  in Biostatistics from Boston University in 2009, where she focused on methods for analyzing cluster randomized trials of academic detailing interventions.  This research was motivated by her work as a supporting statistician in the Division of Pharaco-epidemiology and Pharmaco-economics at Brigham and Women’s Hospital. In 2010 she received at T-32 award from Harvard University to investigate competing risk in total hip replacement. Upon completing her T32, she became a program director and statistician for the Surveillance Research Program at the National Cancer Institute where she worked on survival measures for the SEER registry data and managed a portfolio of grants including the CISNET cooperative agreement. Her research interests are broadly in the application of statistical methods for healthcare data, with particular interest in survival analysis, and nonparametric statistics. In her spare time she enjoys playing with her 2 small children, gardening, and hiking in the great outdoors.

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Sai Liu, MS
Biostatistician

Sai is Biostatistician in Division of Nephrology at Stanford University. She completed her MPH degree in Biostatistics and Epidemiology at the University of Southern California (USC). She then worked at Yale Rudd Center for Food Policy  More & Obesity as a Biostatistician, conducting quantitative and qualitative analyses to examine the extent and impact of exposure to food marketing and weight bias in healthcare. At Stanford, her primary projects include working on the 2016 United States Renal Data System (USRDS) Annual Data Report and analyses of clinical epidemiology, health service and survival outcomes relevant to renal disease using the USRDS and the National Inpatient Sample (NIS). Her statistical interests focus on methods for analyzing missing data, statistical computing, health outcomes and clinical trials.

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