“Clinical trial designs for precision medicine”
Abstract: Precision medicine is revolutionizing medical research and changing the way physicians treat patients based on the fact that there are subgroups of patients who are sensitive to or respond differently to treatments. In this talk, I will present two interesting clinical trial designs for the precision medicine. First, I developed a randomized group sequential enrichment designs to evaluate an experimental treatment against a control based on both early response and survival time and to determine a target treatment-sensitive population. The design starts by enrolling patients under broad eligibility criteria and then alters the entry criteria to restrict to treatment-sensitive patients. For a group sequential monitoring, I propose the optimal adaptive enrichment test statistics which maximizes the power. A simulation study shows that the proposed designs accurately identifies the sensitive subpopulation if it exists, controls type I error and yields substantially higher power compared to a conventional all-comer group sequential design. Second, I proposed a Bayesian adaptive design for avatar-driven clinical trial to find optimal personalized treatment. The latent subgroup membership for avatar-sensitive and avatar-insensitive is considered. I jointly model the toxicity and efficacy outcome for patient with treatment effect for avatar under the latent class model framework and use adaptive randomization to assign patients to effective treatments. A simulation study shows that the proposed design performs well compared to conventional clinical trial without using avatar information for any patient and avatar-driven trial using the avatar with the best performance for the avatar-insensitive patients.
Division of Biostatistics seminars
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