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Discovering the causes of cancer and the means of prevention

Principal Investigators

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Biostatistics Branch (BB)

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

A

  • Albert, Paul S., Ph.D.
    Complex modeling of correlated outcomes in biomedical sciences, including the analysis of longitudinal data, diagnostic testing, and data from biomarker studies

G

  • Gail, Mitchell H., M.D., Ph.D.
    Studies of breast and gastric cancer etiology and risk assessment. Statistical methods for observational and experimental studies, including design and analysis of genetic epidemiologic studies.
  • Graubard, Barry I., Ph.D.
    Use of national surveys and other types of population based-studies for conducting epidemiologic and risk factor surveillance studies related to the incidence of and mortality from cancer. Statistical methods for design and analysis of observational and randomized studies with complex sample designs.

K

  • Katki, Hormuzd A., Ph.D.
    Predicting cervical cancer risk. Estimating absolute risks from studies nested within cohorts. Efficient study designs for comparing diagnostic tests. Methodology for clinical genetics.

L

  • Liu, Danping, Ph.D.
    Developing new statistical methodologies for combining cross-sectional and longitudinal biomarker data for disease prediction, environmental health

P

  • Pfeiffer, Ruth M., Ph.D.
    Statistical methods for: genetic epidemiology, including power assessment of association studies, family data accounting for ascertainment , DNA pooling for joint genotype estimation, and lab data methods.

R

  • Rosenberg, Philip S., Ph.D.
    HIV/AIDS natural history. Viruses and cancer, including HCV, SV40, and KSHV. Meta-analysis in genetic epidemiology. Smoothing and nonparametric regression. Cancer susceptibility.

S

  • Sampson, Joshua, Ph.D.
    Statistical methods for identifying biomarkers, such as genes, proteins, and metabolites, associated with cancer risk. Connecting risk factors with etiology of cancer. High dimensional data analysis.
  • Shi, Jianxin, Ph.D.
    Statistical genetics, with focus on copy number variantion analysis and pathway analysis; statistical analysis of microbiome; analysis of genetic variants related with P53 gene and pathway.

Y

  • Yu, Kai, Ph.D.
    Statistical methods for genetic epidemiologic studies. Tree-based modeling for high dimensional data. Population genetics

Z

  • Zhu, Bin, Ph.D.
    Developing and applying novel statistical methods to increase understanding of biological mechanisms underlying complex diseases, and studying the pattern and determinants of disease in human populations.