The mission of the Department of Biostatistics at St. Jude Children’s Hospital (St. Jude) is to impact advancement of cures and prevention of pediatric catastrophic diseases through biostatistics and data science collaboration and research. Our vision is to become a global leader in biostatistics and data science in catastrophic pediatric diseases and to accelerate discoveries, treatment and quality of care among survivors.
We provide state-of-the-art biostatistical support to all research initiatives at St. Jude by designing and analyzing studies across the spectrum of laboratory research, early and late phase clinical trials, and late-effect and epidemiological studies. Efforts of the department are well supported by 15 faculty members, 24 master's-level biostatisticians, 3 postdoctoral fellows, 8 computing personnel and 5 administrative staff. Research interests of the departmental faculty include design and analysis of clinical trials, statistical genetics and genomics, survival analysis, Bayesian methods, robust methods, longitudinal data analysis, and measurement error models. The department’s research collaboration is further strengthened by dedicated and talented master's-level biostatisticians, some of whom have earned Ph.D. degrees in biological sciences.
The department values collaboration and team approach. Each team, consisting of one or more Biostatistics faculty member and dedicated master's-level biostatistician(s), supports specific research areas assigned to the team. This approach enables the teams to gain disease-specific expertise as well as build relationships with collaborators across the institution. Several faculty members also support collaborative multi-institutional consortia such as the Pediatric Brain Tumor Consortium, the Operations Center of which is housed within the Biostatistics Department; the Children's Oncology Group Brain Tumor Committee; the Childhood Cancer Survivor Study (CCSS), and the Center for Precision Medicine in Leukemia.
Well-designed and well-executed studies provide the best opportunity to advance clinical, translational, and laboratory research at St. Jude. To ensure that St. Jude investigators have access to uniformly high-quality, innovative statistical science for clinical trials designs, the department conducts internal peer review through the Biostatistics Protocol Review Committee, approved by the St. Jude senior leadership, which requires that all St. Jude initiated protocols be reviewed by this committee. Biostatistics also plays a major role in ensuring that the trials conducted at St. Jude are ethical, safe, and compliant with the regulatory requirements by preparing semi-annual reports for the Data Safety Monitoring Board and by assisting in the reporting the study results at ClinicalTrials.gov.
Most of the faculty members in the Department of Biostatistics are also members of the Biostatistics Shared Resource within the St. Jude Comprehensive Cancer Center, with the overarching mission of providing cutting-edge biostatistical support to its members and collaborating with other shared resources to support cancer research.
Department of Biostatistics
MS 768, Room R6030
St. Jude Children's Research Hospital
262 Danny Thomas Place
Memphis, TN 38105-3678
Phone: (901) 595-4986
Fax: (901) 544-8843
Preferred contact method: email
Statistical methods in cancer genomics and genetics
Pediatric hematology and oncology; clinical trial design; survival analysis
Statistical genetics/genomics, statistical modeling of complex data
Statistical analysis of complex imaging data
Statistical analysis of neuroimaging and genetic data; Bayesian statistics
Develop theory and applied statistical methods for imperfectly recalled survival data
Basic, translational, clinical and population science
Phase I-II designs, survival analysis, Bayesian statistics
Develop Bayesian adaptive designs for Phase I-II clinical trials
Develop statistical methods for genomics studies
Clinical trials, robust methods, survival analysis, goodness-of-fit tests
Measurement error and classification, longitudinal modeling, complex epidemiologic study design