Friday, November 4 at 12:30 p.m. in IT 252
Targeted therapy revolutionizes the way that physicians treat cancer and other diseases, enabling them to select individualized treatment adaptively according to the patient’s biomarker profile. The implementation of targeted therapy requires that the biomarkers are accurately measured, which may not always be feasible in practice. We propose two optimal marker-adaptive trial designs in which the biomarkers are subject to measurement errors. The first design focuses on a patient’s individual benefit and minimizes the treatment assignment error so that each patient has the highest probability of being assigned to the treatment that matches his or her true biomarker status. The second design focuses on the group benefit, which maximizes the overall response rate for all the patients enrolled in the trial. We develop a Wald test to evaluate the treatment effects for marker subgroups at the end of the trial and derive the corresponding asymptotic power function. Simulation studies show that the optimal designs proposed achieve our design goal and obtain desirable operating characteristics.
Dr. Zang received his Ph.D. in 2011 from the Department of Statistics and Actuarial Science at the University of Hong Kong. From 2011 to 2014, he finished his Postdoc training at the MD Anderson Cancer Center in the Department of Biostatistics. From 2014 to 2016, Dr. Zang was an assistant professor at Florida Atlantic University’s Department of Mathematical Science. He moved to IU in August of 2016 and is an assistant professor, jointly appointed by the Department of Biostatistics and the Center for Computational Biology and Bioinformatics. Dr. Zang’s research interest is population genetics and adaptive clinical trial design.