Speaker
Description
Clinical trials have become more complex in recent decades. They have gradually become longer, involving more centers and more patients. With these tendencies, interim analyses of ongoing trials have become much more common and much research has been done on the design and analysis of data from adaptive trials. By now group-sequential trials are probably more frequent than single-stage trials.
Until very recently, however, type I error control of the related statistical test decisions was the only result from the research in adaptive designs that has really influenced clinical trial practice. Obviously, the selection of treatment arms at an interim analysis or the narrowing of the patient population to a subpopulation may induce biases as well as affect the coverage probability of confidence intervals. However, these aspects of adaptive designs are largely ignored in practice.
The talk will start out by presenting some examples of trials where this happened and will compare the reported results with various adjusted estimates that were calculated post hoc. It will illustrate how certain subtleties (such as the difference between conditional and unconditional bias-adjusted estimates) influence the adjusted estimates and hence may explain why the research into this topic has not been picked up by practitioners of clinical trials. Part of the explanation lies in the fact that “bias” is a vague term and that selection bias from selecting a dose should not be treated with the same formal techniques as the bias from stopping the trial at a specific point in time. Furthermore, conditional selection biases can become very large, whereas unconditional selection biases oftentimes remain modest. The talk will conclude with a discussion of what we may want to change in the future.
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