Using Surrogate Endpoints for Decision-Making in Adaptive Seamless Designs

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Clinical trials are used to evaluate the safety and effectiveness of new drugs and other novel therapies prior to general use. Conventionally, a major part of this evaluation is a trial to compare one new drug (at a single prespecified dose/formulation) with an existing standard or control treatment. Statistical methods have been developed over many years to allow valid interpretation of the data from such trials.A recent innovation in clinical trial design has been the development of new approaches in which several new treatments are compared simultaneously with the control in a single clinical trial, with the less effective treatments dropped from the trial on the basis of early results. Such methods, called adaptive seamless designs, have the potential to reduce the number of patients required. A particular challenge arises in chronic diseases when the data indicating treatment effectiveness may be observed only after long-term follow-up. For example, in Multiple Sclerosis the primary outcome considered might be a change in health status after three years of treatment. Often some other earlier outcome can be observed that it is believed is predictive of the primary long-term outcome. Such an outcome is called a surrogate endpoint. In the Multiple Sclerosis case, for example, imaging of the brain can give an early indication of disease progression. In this case it would seem sensible to use this surrogate endpoint information in addition to any available primary endpoint data to decide which treatments should be dropped from the trial. The development of statical methods that allow the use of surrogate endpoints for treatment selection in adaptive seamless designs has been an area of active research in the last few years. The difficulty comes when trying to ensure that the final comparison of the selected treatment (or treatments) is a fair one, since they could have been selected because they appeared superior to other treatments by chance.The aim of this project is to develop a new statistical method for using a surrogate endpoint in an adaptive seamless design. By making the most efficient use of the surrogate endpoint information, this new approach will lead to further reduction in the number of patients required in the trial. This will reduce the number of patients exposed to less effective treatments and ensure that effective new drugs are made available for general use, on the basis of thorough evaluation, as quickly as possible.