American Statistical Association
Many major genes have been identified that strongly influence the risk of cancer. It is thus critical to identify which genetic mutations confer an increased risk and which ones are harmless, so that one can appropriately counsel carriers of these mutations. This is a challenging task, since there can be numerous mutations in the gene and relatively little evidence available about each individual mutation. Capanu et al. (2008) employed hierarchical modeling using the pseudo-likelihood method to estimate the relative risks of individual rare variants and showed that one can draw strength from the aggregating power of hierarchical models to distinguish between variants that are harmful and those that are harmless. In this talk, we investigate the validity of this hierarchical modeling approach using simulations based on two real datasets from melanoma and breast cancer.
This is joint work with Colin Begg.
|Date:||Wednesday, September 9, 2009|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.