Past Events

NIH Human Subjects Research Protections Program – Updates for DCEG Studies - Drs. Savage and McMaster

DCEG Seminar

Drs. Savage and McMaster will provide an update on the central NIH IRB, Non Human Subject Research (NHSR), and NCI Ethical Review Panel review processes for DCEG studies. All are highly encouraged to attend to receive clarifications on these processes.

Estimating Somatic Variant Richness & Using the “Hidden” Genome to Improve Classification of Cancer Types - Dr. Chakraboty

Biostatistics Branch Seminar Series

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3-5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA.

Methods for Clinical Decision-Making: Risk Models with Polygenic Risk Scores - Dr. Meisner

Biostatistics Branch Seminar Series

This research lies in the development of statistical methods that can be used to inform clinical decision making. These methods aim to provide actionable, relevant information to patients and clinicians such as risk of a particular disease or response to a given treatment. In this seminar, Dr. Meisner will outline the broad themes of her research, discuss in detail two recent projects related to polygenic risk scores (PRS), and touch on possibilities for future work, including methods for PRS as treatment selection tools.

Various Usage of Influence Functions for Complicated Statistics in the Context of Survey Sampling - Dr. Yu

Biostatistics Branch Seminar Series

Seminar: Publicly available national survey data are useful for the evidence-based research to advance our understanding of important questions in the health and biomedical sciences. Dr. Yu discusses methods of appropriate variance estimation.

From Genome-Wide Association Studies to Biological Insights Using Integrative Molecular Epidemiology - Dr. Kraft

DCEG Seminar

Genome-wide association studies (GWAS) have identified thousands of common genetic variants robustly associated with risk of cancer, yet the biological mechanisms underlying these associations remain largely unknown, in part because of confounding by local correlation among variants, and in part because most of these variants are located in non-coding regions with poorly-understood functionality. Methods that integrate GWAS results with auxiliary data can suggest biological hypotheses for further in silico, in vitro and in vivo studies.

Next Generation Statistical Methods in the Post-Genome Wide Association Studies - Dr. Chatterjee

Biostatistics Branch Seminar Series

In the early years of genome-wide association studies, data analysis primarily relied on fairly simplistic methods, such as running millions of univariate linear or logistic regressions, one for each genetic variant. Recently, however, as the sample sizes for some GWAS have become extremely large and various types of other genomic data have increasingly become available, analysis of such data has also become much more complex and statistically sophisticated.

Longitudinal Data - Dr. Albert, Part 1 of 3

Biostatistics at the Frontier Seminar Series

Part 1 of 3 of the 2020 Biostatistics at the Frontier Seminar Series.

Chasing Rainbows: Building a Career in Sexual and Gender Minority Health Equity Science - Dr. Kamen

Fellows' Cancer Health Disparities Interest Group

Dr. Kamen's research has focused on factors that lead to health disparities among sexual and gender minority populations, specifically disparities in cancer-related health outcomes and psychological distress

Statistically Consistent Saliency Estimation - Dr. Barut

Biostatistics Branch Seminar Series

Emerging Cancer Health Disparities - Dr. Gomez

Descriptive Epidemiology Seminar Series

Dr. Scarlett Lin Gomez is an epidemiologist with research interests in the role of social determinants of health, including race/ethnicity, socioeconomic status, gender, immigration status, sociocultural factors, and neighborhood contextual characteristics, on health outcomes.