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Ming T. Tan, Ph.D., is professor of Epidemiology and Preventive Medicine and Head of the Division of Biostatistics of the University of Maryland Marlene and Stewart Greenebaum Cancer Center (UMGCC). He has been on the faculty since 2002. He is a member of the center's Experimental Therapeutics Program. He was previously a senior member (faculty) at St. Jude Children's Research Hospital Cancer Center and biostatistics director of St Jude's Developmental Therapeutics for Solid Malignancies Program (1997-2002), assistant and associate professor of Biostatistics and Epidemiology at The Cleveland Clinic Foundation (1990-1997). He received his Ph.D. in statistics from Purdue University, Indiana.
Dr. Tan has extensive collaborative research experience in the design, conduct and analysis of clinical trials (in both multi-center and single institutional settings), laboratory investigations and epidemiological research. He is also the Principal Investigator (PI) of the NCI R01 grant on the Design & Analysis of Preclinical Combination to develop innovative methods to optimally design and efficiently analyze pre-clinical drug combination therapies in cancer by integrating concepts in modern statistical and number-theoretic methods and pharmacology. He was PI of the NCI RO3 grant on “Design and Analysis of Cancer Epidemiology Studies” and PI on NIH R01 grant on "Clinical Trial Design and Interim Analysis." Dr. Tan has served multiple Data and Safety Monitoring Boards for clinical trials.
He is a member of FDA Advisory Committee and of multiple NIH study sections (such as Clinical Oncology and Epidemiology of Cancer), review and site visit panels (such as that for SPOREs and P30). He is a Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. Dr. Tan is Associate Editor of Statistics in Medicine and Drug Design, Development and Therapy. He has also served on the editorial board of Biometrics.
Dr. Tan's research interests include clinical trial design and interim analysis, statistical methods to enhancing pivotal trial design using preclinical (xenograft) models that are predictive of clinical outcomes; statistical methods in molecular genetics and molecular pharmacology and in cancer drug development; Clinical trials designs with group sequential methods; Analysis of longitudinal data with continuous or ordinal or discrete outcome including mixed-effects models; Model adequacy checking; Methods to evaluate accuracy of diagnostic tests and biomarkers and cancer epidemiology; Bayesian hierarchical modeling and applied Bayesian methods.
Hongbin Fang, Ph.D., is a UMGCC faculty biostatistician and an assistant professor of Epidemiology and Preventive Medicine. After he received his Ph.D. in statistics from Hong Kong Baptist University in 1998, Dr. Fang worked on statistical methods on survival models as related to AIDS research at University of Missouri as a postdoctoral fellow and then joined the Department of Biostatistics at St. Jude Children's Research Hospital in 2000 as a postdoctoral research associate. He joined UMGCC in 2002 and has collaborated with investigators in several research programs of the cancer center. He is a member of the center's Molecular and Structural Biology Program.
His research interests include experimental design and statistical analysis for drug development, statistical methods for radiation biology and oncology, interval censored survival and multivariate survival analysis models.
Olga Goloubeva, Ph.D., M.Sc., is a UMGCC senior cancer biostatistician. Dr. Goloubeva received her Ph.D. in engineering. She continued her graduate training in mathematical statistics and in 1999 earned a Master's degree in statistics from Dalhousie University, Halifax, Canada. She was assistant professor in the Department of Mathematics and Computer Studies at the Mount Saint Vincent University, Halifax, Canada (1993-1999). In 1999, she joined St. Jude Children's Research Hospital Cancer Center as a biostatistician, where she collaborated with investigators in virology, diagnostic imaging, and radiation oncology. In 2001, Dr. Goloubeva moved to Dana Farber Cancer Institute, Boston, MA, where she collaborated as a biostatistician in CLL Consortium, and the Leukemia Committee for ECOG, and the AIDS Immunology Group. She joined UMGCC in 2003 and has been involved in collaborative research throughout the center, including areas in breast, prostate, lung cancer, myeloma, radiation oncology, and cancer disparities. She is currently a member of the UMGCC Tissue Bank Committee a member of the center's Hormone Responsive Cancers Program.
Guo-Liang Tian, Ph.D., is a UMGCC faculty biostatistician and an instructor of Epidemiology and Preventive Medicine. Dr. Tian was trained under Dr. K.T. Fang, an internationally renowned statistical scientist in multivariate analysis and experimental design, at Hong Kong Baptist University. He earned his Ph.D. degree in 1998 from Academic Sinica and his postdoctoral degree from Peking University in 2000. He joined the Department of Biostatistics at St. Jude Children's Research Hospital Cancer Center in May of 2000 as a post-doctoral research associate. His research areas include generalized mixed-effects models for longitudinal data, hierarchical modeling, and applied Bayesian methods in biostatistical models.
Zhenqiu Liu, Ph.D., is a UMGCC faculty biostatistician and an assistant professor of Epidemiology and Preventive Medicine. He received his Ph.D. in Management Science (Operations Research) with concentration in Data Mining and M.S. in Computer Science, both from The University of Tennessee. Previously Dr. Liu worked as a postdoctoral research fellow at the Bioinformatic Cell of U.S. Army Medical Research and Material Command in 2003-2004 and at the Department of Statistics, The Ohio State University in 2004-2005.
His research interests include bioinformatics, statistical genetics, and data mining. He has extensive experience in microarray, mass-spectrometry, SNP, and DNA sequence data analysis. Currently he is concentrating on meta-analysis and dynamic pathway modeling in cancer research.
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