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The question of selecting the "best" amongst different choices is a commonproblem in statistics. In drug development, our motivating setting, thequestion becomes, for example: what is the dose that gives me a pre-specifiedrisk of toxicity or which treatment gives the best response rate. Motivated bya recent development in the weighted information measures theory, we propose anexperimental design based on a simple and intuitive criterion which governs armselection in the experiment with multinomial outcomes. The criterion leads toaccurate arm selection without any parametric or monotonicity assumption. Theasymptotic properties of the design are studied for different allocation rulesand the small sample size behaviour is evaluated in simulations in the contextof Phase I and Phase II clinical trials with binary endpoints. We compare theproposed design to currently used alternatives and discuss its practicalimplementation.

An information-theoretic approach for selecting arms in clinical trials