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Discovering the causes of cancer and the means of prevention

Biostatistics Branch Research Areas

Investigators in the Biostatistics Branch (BB) apply advanced statistical methods and data resources for many types of studies, described below.

Descriptive Epidemiology Studies

Descriptive epidemiology studies characterize cancer incidence and mortality temporal trends, age-specific rates, geographic distribution of cancer, race and ethnic differences in cancer rates, and birth cohort effects. BB investigators utilize novel analytic tools to obtain etiologic clues from descriptive studies, for example:

  • Identify novel data sources through national and international collaboration (e.g. ACS, IARC, Danish breast cancer group)
  • Integrate novel analytic tools to obtain etiologic clues from descriptive studies (e.g. Modeling age-period-cohort effects and etiologic heterogeneity)
  • Perform record linkage studies, using SEER and other population-based databases (e.g. SEER-AIDS and SEER-Transplant studies, NHANES-NDI)

Analytical Studies

Increased participation in domestic and international consortia has yielded large-scale datasets and biospecimens, supporting various types of analytic studies:

Collaboration Across Major DCEG Studies

BB investigators collaborate in major studies across DCEG, providing statistical and analytical leadership.  Selected studies include:

Access an integrated transcript and audio-described version of "DCEG's Commitment to Collaboration" video.

DCEG's Commitment to Collaboration

DCEG’s entire team is deeply integrated to create innovative science aimed at improving human health and training. 

Translational Research

BB investigators strive to apply what is learned from research discoveries and findings to practical applications in support of public health.  Examples of these efforts include:

Methodological Research

BB maintains active methodological research programs in a wide range of topics that directly relate to analytic problems encountered in DCEG and at NCI, more generally. Investigators publish their methodological research in leading statistical, biostatistics, and bioinformatics as well as epidemiology, genetics, and clinical journals. Examples of research topics include:

  • New approaches for cancer risk prediction that are used for counseling and screening recommendations
  • Sampling-based designs and analysis approaches for generalizing the results of a cohort study to a representative population
  • Development of new methods for assessing complex environmental exposure (possibly, longitudinally) on cancer incidence
  • Novel approaches for the analysis of microbiome, metabolomic, and genomic biomarkers
  • New methods for characterizing identifying both germline and somatic mutational patterns in the genome
  • Modeling natural history of cancer progression through precursors and stages of cancer using stochastic modeling
  • Novel approaches for cancer surveillance using age-period-cohort and other methodology
  • Statistical methods that account for errors in linkage when analyzing cohort data in which the outcomes are obtained through linkage to a large registry
  • Novel design strategies for pooling or combining samples for disease screening or prevalence estimations