A Sequential Conditional Test for Medical Decision Making

Friday, September 15, 2017 - 3:50pm - 4:20pm
Keller 3-180
Min Qian (Columbia University)
Due to patient heterogeneity in response to various aspects of any treatment program, biomedical and clinical research has shifted from the traditional one-size-fits-all treatment to personalized medicine. An important step in this direction is to identify the treatment and covariate interactions. We consider the setting in which there are a potentially large number of covariates of interest. Although a bunch of novel variable selection methodologies are being developed to aid in treatment selection in this setting, few, if any, has adopted formal hypothesis testing procedures. In this talk, I will present a bootstrap based testing procedure which can be used to sequentially identify variables that interact with treatment. The method is shown to be effective in controlling type I error rate with a satisfactory power as compared to competing methods.