Dirk Moore, PhD
Dirk Moore, Ph.D., is an associate professor in the Department of Biostatistics and Epidemiology at the Rutgers School of Public Health. He is also a member of the Cancer Institute of New Jersey. Prior to joining Rutgers, Dr. Moore was a faculty member in the Department of Statistics at Temple University and has held visiting positions at the Radiation Effects Research Foundation in Hiroshima and at the Division of Cancer Epidemiology and Genetics at the National Cancer Institute. Dr. Moore received his doctoral degree in biostatistics from the University of Washington.
Dr. Moore uses cancer mortality data derived from administrative databases to study effects of treatment options on survival. For example, he has used survival analysis methods with data in the SEER-Medicare database to understand and compare treatment options, and to predict the course of prostate cancer. A widely used published result presents a set of competing risks mortality profiles for use by patients newly diagnosed with prostate cancer. Other publications of Dr. Moore’s have addressed comparison of treatment options for prostate cancer using instrumental variable analysis and propensity-matching methods and quantifying the long-term complications of alternative treatments. He has used the National Cancer Data Base to more accurately classify pancreatic cancer tumors and to study the effects of chemotherapy and radiation on the progression of pancreatic cancer. He has also collaborated extensively on the design and analysis of numerous cancer and non-cancer clinical trials. Dr. Moore collaborates on a multi-year funded project with a research group using mass spectrometry to assess protein abundance on a large scale using spectral counting, iTraq technology, and other techniques. This has yielded numerous publications identifying differential expression rates of large numbers of proteins. A notable additional project has involved using iTraq and other methodology to identify the subcellular locations of proteins. His contribution has been to develop multivariate statistical analysis methods to identify proteins that co-locate in multiple compartments. The work has culminated in a tool that researchers can use to identify these co-locations and other proteins that locate with them and in a software package that allows researchers to use the methods on their own data.