Abstract:
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In this presentation, we discuss methods for testing hypotheses associated with two endpoints (either multiple primary endpoints or one primary and one secondary endpoint) in group-sequential clinical trials comparing a test intervention to a control intervention. We consider the specific situation where the null hypothesis for one endpoint has been rejected at some interim and investigate the other as (1) co-primary or (2) ordered endpoints. Though maximum number of analyses is usually prefixed, a promising but nonsignificant interim result sometimes leads more frequent data review at the request of a data monitoring committee, which results in inflation of the Type I error probability. While changing the frequency of subsequent analyses based on the observed data is an attractive option, in practice it would be important to consider how the Type I error probability can be controlled, and then, whether the adaptation is efficient. We discuss the impact of these adaptations on power and sample size in relation to the Type I error probability, including methods for managing inflation of it. We illustrate the approaches using a real clinical trial.
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