2 July 2012, 12 pm
Canynge Hall, Room LG.08
Jeremy Taylor is the director of the University of Michigan Cancer Center Biostatistics Unit. He is director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England, the University of Adelaide and INSERM, Bordeaux. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.
We consider the problem of identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. The method involves predicting response probabilities of both potential outcomes for treatment and control for each subject using a random forest model. The difference in these predictions is then used as the outcome in a classification or regression tree, which can potentially include any set of the covariates. Methods of obtaining honest estimates of the selected subgroup will be discussed.
The seminar is free and all are welcome (including members of the public) without needing to book a place. If you have difficulties with stairs, we have a lift to provide access to the lower ground floor.