Past Events

Collaborating Across Disciplines for Cancer Discovery and Translation - Dr. Platz

DCEG Visiting Scholar Seminar

Modern Genetic Analysis on The Cloud - Dr. Hartley

Biostatistics Branch Seminar Series

Harnessing Rare Somatic Variants for Tumor Classification - Shen

Biostatistics Branch Seminar Series

In recent years there has been extensive investigation of the mutational landscape of the cancer genome from large-scale sequencing studies. However, the bulk of the attention has focused on major cancer genes, and especially the hot spot mutations in these genes at which mutations occur frequently.

Novel Statistical Approach to Handle Limit of Detection in Environmental Mixture Analysis - Dr. Zhao

Biostatistics Branch Seminar Series

impact of chemical mixtures on human health since human beings are exposed to a variety of chemicals, through food, water, air and consumer products.

Studies of Occupation and Cancer: Etiology and Application of Molecular Pathology - Dr. Stella Koutros

DCEG Tenure-Track Seminar

Knowledge Discovery with Bayesian Rule Learning Methods for Actionable Biomedicine - Mr. Balasubramanian

DCEG Seminar

Discovery of precise biomarkers are crucial for improved clinical diagnostic, prognostic, and therapeutic decision-making. They help improve our understanding of the underlying physiological (and pathophysiological processes) within an individual.

Building and Leveraging Data Repositories Over 25+ Years: Creating Unique Cohort Subsets - Dr. Cullen-Meyer

DCEG Seminar

Leveraging the Cancer Prevention Study Cohorts to Clarify Breast Cancer Risk Factors - Dr. Gaudet

DCEG Seminar

Leveraging the Cancer Prevention Study Cohorts to Clarify Breast Cancer Risk Factors

Evidence for Familial Clustering of Breast Cancer Age at Onset - Dr. Weinberg

DCEG Events

Biased Sampling, Left Truncation and Survival Analysis, Lecture II - Dr. Jing Qin

Biostatistics Branch Seminar Series

Biased sampling occurs when a proper randomization cannot be achieved; in this case, the observed sample will not be representative of the population of interest. Biased sampling problems appear in many areas of research, including, medicine, epidemiology and public health, social sciences, and economics.