Data from randomized controlled clinical trials (RCT) are considered to be the best source of information in cancer research. However, the strength of RCTs can be hampered by their (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. Such studies, however, are not without their own set of challenges.
The complexity of sampling mechanisms and various biases associated with prospective observational studies raise considerable analytical challenges in both study design and data analysis. The peril of selection bias is exacerbated in many cohort studies. To address these challenges, we need practical statistical designs and innovative analytic approaches to evaluate clinical effectiveness and healthcare interventions outside of controlled clinical trials. Dr. Shen will show examples of RCTs and observational cohort studies, and describe challenges and opportunities in analyzing data from such studies.
Yu Shen, PhD, is Professor of Biostatistics at The University of Texas M.D. Anderson Cancer Center, where she holds the Conversation with a Living Legend Professorship. She was elected as a fellow of the American Statistical Association in 2007, and has served as Associate Editor for three major biostatistics journals. Dr. Shen's research interests include development of statistical methods in cancer screening and prevention studies, clinical trial design, health services research, and statistical applications in cancer research. She has been Principal Investigator on numerous statistical methodology research projects, including health services research, and has received continuous NIH funding.