Adaptive designs for clinical trials with prognostic factors that maximize utility
Seminar Room 1, Newton Institute
AbstractThe talk concerns a typical problem in Phase III clinical trials, that is when the number of patients is large. Patients arrive sequentially and are to be allocated to one of $t$ treatments. When the observations all have the same variance an efficient design will be balanced over treatments and over the prognostic factors with which the patients present. However, there should be some randomization in the design, which will lead to slight imbalances. Furthermore, when the responses of earlier patients are already available, there is the ethical concern of allocating more patients to the better treatments, which leads to further imbalance and to some loss of statistical efficiency. The talk will describe the use of the methods of optimum experimental design to combine balance across prognostic factors with a controllable amount of randomization. Use of a utility function provides a specified skewing of the allocation towards better treatments that depends on the ordering of the treatments. The only parameters of the design are the asymptotic proportions of patients to be allocated to the ordered treatments and the extent of randomization. The design is a sophisticated version of those for binary responses that force a prefixed allocation. Comparisons will be made with other rules that employ link functions, where the target proportions depend on the differences between treatments, rather than just on their ranking. If time permits, the extension to binary and survival-time models will be indicated. Mention will be made of the importance of regularization in avoiding trials giving extreme allocations. A simulation study fails to detect the effect of the adaptive design on inference. (Joint work with Atanu Biswas, Kolkata)
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