|
Abstract:
|
I will begin with a brief overview of my involvement in the development of adaptive designs. Then I will describe the Randomized Reinforcement Design. Urn models are useful when randomization is important. Treatment assignments correspond to the color of a drawn ball. If balls are added or taken away depending on outcomes, the urn is "response-driven." The Randomized Reinforcement Urn (RRU) is optimal in the sense that it assigns patients to the best treatment with probability converging to one. No other urn model in the literature has this property. The RRU was first studied by Durham, Flournoy and Li (1998) for binary random variables. However, because allocation proportions degenerate to 0 or 1 (or to a random variable if treatments are equal), theory that permits testing treatment differences has only recently been obtained (May & Flournoy, 2008).
|
