An Up-and-Down (UD) experiment for estimating a given quantile of a binary response curve is a sequential procedure whereby at each step a given treatment level is used and, according to the outcome of the observations, a decision is made (deterministic or randomized) as to whether to maintain the same treatment or increase it by one level or else to decrease it by one level. The design points of such UD rules generate a Markov chain and the mode of its invariant distribution is a good approximation to the quantile of interest. The main area of application of UD algorithms is in Phase I clinical trials, where it is of greatest importance to be able to attain reliable results in small-size experiments. Nonetheless, relatively little attention has been paid in the literature to the study of the speed of convergence and precision of quantile estimates of such procedures. In this paper we address these issues, both in theory and by simulation. We prove that the version of UD designs introduced in 1994 by Durham and Flournoy can in a large number of cases be regarded as optimal among all UD rules. Furthermore, in order to improve on the convergence properties of these algorithms, we propose a second-order UD experiment which, instead of making use of just the most recent observation, bases the next step on the outcomes of the last two. This procedure shares a number of desirable properties with the corresponding first order designs, but also allows greater flexibility and, with a suitable choice of the parameters, can lead to an improvement of quantile estimates.

Up and Down experiments of first and second order

BORTOT, PAOLA;GIOVAGNOLI, ALESSANDRA
2005

Abstract

An Up-and-Down (UD) experiment for estimating a given quantile of a binary response curve is a sequential procedure whereby at each step a given treatment level is used and, according to the outcome of the observations, a decision is made (deterministic or randomized) as to whether to maintain the same treatment or increase it by one level or else to decrease it by one level. The design points of such UD rules generate a Markov chain and the mode of its invariant distribution is a good approximation to the quantile of interest. The main area of application of UD algorithms is in Phase I clinical trials, where it is of greatest importance to be able to attain reliable results in small-size experiments. Nonetheless, relatively little attention has been paid in the literature to the study of the speed of convergence and precision of quantile estimates of such procedures. In this paper we address these issues, both in theory and by simulation. We prove that the version of UD designs introduced in 1994 by Durham and Flournoy can in a large number of cases be regarded as optimal among all UD rules. Furthermore, in order to improve on the convergence properties of these algorithms, we propose a second-order UD experiment which, instead of making use of just the most recent observation, bases the next step on the outcomes of the last two. This procedure shares a number of desirable properties with the corresponding first order designs, but also allows greater flexibility and, with a suitable choice of the parameters, can lead to an improvement of quantile estimates.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/20800
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