Skip to main content

Myra Samuels Memorial Lecture

Personalized Dose Discovery Using Outcome Weighted Learning

Michael R. Kosorok
University of North Carolina at Chapel Hill

Start Date and Time: Fri, 10 Apr 2015, 10:30 AM

End Date and Time: Fri, 10 Apr 2015, 11:30 AM

Venue: WTHR 320

Refreshments: HAAS 111 at 9:30 AM


In dose-finding clinical trials, there is a growing recognition of the importance of considering individual level heterogeneity when searching for optimal doses. An optimal individualized dose rule (IDR) should maximize the expected clinical benefit. In this talk, we consider a randomized trial design where candidate dose levels are continuous. To find the optimal IDR under such a design, we propose an outcome weighted learning method which directly maximizes the expected beneficial clinical outcome. A difference of convex functions (DC) algorithm is adopted to efficiently solve the associated non-convex optimization problem. The consistency and convergence rate for the estimated IDR are derived and small-sample performance is evaluated via simulation studies. We demonstrate that the proposed method outperforms competing approaches. Finally, we illustrate the method using data from a cohort study for Warfarin (an anti-thrombotic drug) dosing.

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2018 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue? Please contact the College of Science Webmaster.