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NCI: DCTD: Biometric Research Branch
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Recent Research

  • Development of methods for identifying genetic networks from DNA-probe-array generated gene expression data.
  • Mathematical model of apoptosis and resistance mechanisms in cancer chemotherapy
  • Evaluation of prognostic importance of molecular markers (e.g. p53, erbB2 , Ki67 etc) in a large series of node negative breast cancer patients with long follow-up.
  • Development of improved methods of prognostic prediction: neural network models for time-to-event data, regression splines for studying how prognosis varies with marker level, methods for selection and evaluation of optimal cut-point for markers, cross-validation for model selection, Bayesian proportional hazards models, measures of explained variation for time-to-event data.
  • Analyses of factors which effect responses of tumor cell lines to a wide range of chemical compounds, e.g. mechanism of action, histology, MDR, P53. Development and application of pattern recognition analytic and visualization methods for studying multivariate relationships in this large database.
  • Development of Bayesian methods for the design and analysis of 2 x 2 factorial trials.
  • Development of Bayesian methods for dealing with multiple comparison problems. Applications to evaluating associations between tamoxifen and second cancers of G.I. tract. Application to analysis of genetic linkage studies. Application to analysis of cytogenetic abnormalities in patients with melanoma or ovarian cancer.
  • Development and application of randomized selection designs as alternatives to phase III designs for therapeutic development in rare diseases
  • Development of Bayesian sequential designs for monitoring efficacy and toxicity in phase II trials of drug combinations using historical controls.
  • Development of more flexible sequential monitoring designs to adapt the sample size of a randomized trial to the magnitude of the treatment effect.
  • New methods for the analysis of complex health survey data.
  • New methods for the analysis of quality of life data in clinical trials.
  • Mathematical models of the relationship between chemotherapy schedules and dose intensities and effects.
  • New methods for the analysis of spatially correlated binary and count data.
  • Research in molecular statistics and bioinformatics section

 

Please send comments and suggestions to Mailto buttonbrb@linus.nci.nih.gov

last updated: Feb. 15, 2002