Past Events

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.

Diet and Cancer: Harnessing Emerging Technologies to Advance Etiologic Research and Improve Nutritional Assessment - Dr. Loftfield

DCEG Stadtman Seminar

Diet and Cancer: Harnessing Emerging Technologies to Advance Etiologic Research and Improve Nutritional Assessment

Comparing Alternatives for Estimation from Nonprobability Samples - Dr. Valliant

Biostatistics Branch Seminar Series

Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly-robust estimation. In the first, the sample is treated as if it was obtained via a probability mechanism but, unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population.