CRAB’s statistical team is comprised of statisticians with a wide variety of backgrounds and expertise. Our statistical team has decades of experience in the field of oncology clinical development, with specific experience designing, analyzing, reporting, and representing important findings in the field of oncology. Many of our staff have affiliate appointments at the Fred Hutchinson Cancer Research Center and the Biostatistics Department at the University of Washington.
We have experience in the design and analysis of a wide variety of oncology clinical trials with associated translational data. Trials include single- institution oncology trials, cooperative group SWOG trials, as well as Phase I-IV industry sponsored trials. In addition to designing and analyzing trials, CRAB statisticians are involved with cutting-edge methodological research on novel clinical trial designs and have led or contributed to many publications in that area.
John Crowley, Ph.D., President and Chief Executive Officer, brings four decades of experience in Biostatistics to his role as President and CEO of Cancer Research And Biostatistics (CRAB). Under Dr. Crowley's leadership, CRAB serves as the Statistical Center for the Southwest Oncology Group, a federally sponsored clinical research consortium comprised of thousands of cancer centers and investigators across the nation. CRAB also serves as the Statistical Center for the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences, the world's most successful research and treatment facility for myeloma. To serve these groups, Dr. Crowley oversees the study design, protocol development, data management, quality control, data analysis, and statistical research of more than 100 active multi-site clinical cancer trials. CRAB employs a staff of 75 high- level individuals including statisticians, data coordinators and other professionals dedicated to conquering cancer through large-scale clinical trials. 
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William Barlow, Ph.D., Senior Biostatistician, is the lead statistician for the SWOG Breast Committee designing, conducting, and analyzing breast cancer clinical trials. In the latest study to be launched nationwide we will study whether the 21-gene recurrence assay can predict whether chemotherapy is beneficial in women with early stage node-positive, hormone receptor positive breast cancer. This study will include testing tumors in 9,000 women to identify 4,000 women who meet the requirements for the trial. Such biomarker evaluations can direct therapy – to this end he is involved in a grant with the Fred Hutchinson Cancer Research Center to develop methods for testing biomarkers in the clinical setting. Other interests include breast cancer screening, other clinical prognostic and predictive markers, trial design, and efficient sampling methods for biomarker evaluation. CV pdf
Antje Hoering, Ph.D, Chief Scientific Officer, has a unique and comprehensive background in applied and methodological research in biostatistics, encompassing areas such as the design and analysis of oncology clinical trials, as well as the analysis of a wide variety of correlative data. Dr. Hoering is the lead statistician of the SWOG Myeloma Committee, the SWOG Early Therapeutics Subcommittee and the Stand Up To Cancer, Pancreatic Dream Team. She is the coordinating statistician for the Myeloma Institute for Research and Therapy, where CRAB serves as the Biostatistics Core of a Program Project grant. She is also the coordinating statistician for the International Myeloma Committee and the Pancreatic Cancer Research Team. She serves as a consultant on a variety of industry sponsored studies and has been the biostatistics representative on two Type B meetings with the FDA. Other responsibilities include supervision and mentoring of statisticians, programmers and summer interns. Dr. Hoering holds affiliate appointments at the Fred Hutchinson Cancer Research Center and the Biostatistics Department of the University of Washington. CV pdf
Pingping Qu, Ph.D., Senior Biostatistician, is the CRAB expert on high-dimensional data analysis. She analyzes large amounts of Gene Expression Profile (GEP) data, often taken at various time-points of a treatment protocol, correlating these data to clinically relevant outcomes. She also has extensive expertise in developing risk scores based on GEP data. Her experience also includes the analysis of single nucleotide (SNP) data and proteomics data.
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