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A C Atkinson

Publications and source records attributed to A C Atkinson.

4 recordsLinked to original sources

Optimum biased-coin designs for sequential treatment allocation with covariate information.

Randomized optimum designs of biased-coin type are compared with other strategies for the sequential allocation of two or more treatments in a clinical trial. The emphasis is on the variance of estimated treatment contrasts. This variance, which depends on the design strategy employed, may be interpreted as the number of patients on whom information is lost. Simulations provide clear plots of the evolution of this loss during the course of the clinical trial.

Bias↗

Optimum experimental designs for multinomial logistic models.

Multinomial responses frequently occur in dose level experiments. For example, in a study of the influence of gamma radiation on the emergence of house flies (Musca domestica L., 1758), three disjoint outcomes occurred: death before the pupae opened, death during emergence, and life after emergence. Although the flies are easy to breed, this sort of bioassay is, in general, very expensive since it requires the use of a gamma radiation source. Experiments therefore need to be designed to involve the minimum number of different doses. Here the theory of optimum experimental design is applied to provide efficient experiments to estimate the parameters of those multinomial logistic models that are a special case of the multivariate logistic models of Glonek and McCullagh (1995, Journal of the Royal Statistical Society, Series B 57, 533-546). The purpose is to reduce the overall experimental cost. The general equivalence theorem (Fedorov, 1972, Theory of Optimal Experiments) is adapted to this class of models, providing an effective method of generating and checking the optimality of designs. One example on flies demonstrates the method, which can be easily implemented.

Animals↗

Optimum experimental designs for properties of a compartmental model.

Three properties of interest in bioavailability studies using compartmental models are the area under the concentration curve, the maximum concentration, and the time to maximum concentration. Methods are described for finding designs that minimize the variance of the estimates of these quantities in such a model. These methods use prior information. Both prior estimates and prior distributions are used. The designs for an open one-compartment model are compared with the corresponding D theta-optimum design for all parameters and also with designs that minimize the sum of the scaled variances of the individual properties.

Animals↗