Seminar by Prof. Yu Shen, "Statistical challenges and promises: big data and sampling biases"

Dear All,

Ecology and Evolution Unit (Mikheyev Unit) would like to invite you to a seminar by Prof. Yu Shen.

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Date: Monday, July 13th ,  2015
Time: 14:00-15:00
Venue: D015, Level D,Lab1
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Speaker: Professor Yu Shen

Short Bio: Dr. Yu Shen is a Professor of Biostatistics at the University of Texas M.D. Anderson Cancer Center, where she holds the Conversion with a Living Legend Professorship. She is a fellow of American Statistical Association. Dr. Shen has actively collaborated with oncologists, cancer prevention specialists, and cancer epidemiologists by serving as a lead statistician on cancer prevention and treatment clinical trials and observational studies. Dr. Shen has served as the Biostatistics Core Director for Bladder Cancer SPORE at MDACC since 2001. Dr. Shen has served as an associate editor for major biostatistics journals: Biometrics, and Life Data Analysis.

Title: Statistical challenges and promises: big data and sampling biases
Abstract: Data from controlled clinical trials are considered to be the best source of information in cancer research. The analysis of a randomized controlled trial (RCT) tends to be straightforward due to the rigorous design of such studies. However, the strengths of the RCT can be hampered by its (possibly) limited applicability, long duration, and high cost. An alternative source of data can be found in the large observational databases and longitudinally-followed patient cohorts that have emerged. These invaluable resources present new opportunities in research to provide potential insights into cancer treatment and patient care. However, such studies are not without their own set of challenges.
The complexity of sampling mechanisms and various biases associated with prospective observational studies raise considerable statistical challenges in both the design and the data analysis. Standard analysis methods and design tools for clinical trials are often not applicable and, in fact, are invalid for prospective cohort studies. To address the above challenges, we need practical statistical designs and innovative analytic approaches to evaluate clinical effectiveness and healthcare interventions outside of controlled clinical trials. I will show examples of observational cohort studies and describe challenges in analyzing data from such studies. I will provide practical tools for estimating sample size, and innovative methods for analyzing time-to-event data observed from prevalent cohort studies.

We look forward to seeing many of you at the seminar.

Sincerely,
Yoko Fujitomi
Ecology and Evolution (Mikheyev) Unit