Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

Nicolás M. Ballarini, Thomas Burnett, Thomas Jaki, Christoper Jennison, Franz König, Martin Posch

Research output: Contribution to journalArticlepeer-review

Abstract

We design two-stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision-theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per-comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

Original languageEnglish
JournalStatistics in medicine
Early online date29 Mar 2021
DOIs
Publication statusE-pub ahead of print - 29 Mar 2021

Keywords

  • Bayesian optimization
  • conditional error function
  • subgroup analysis
  • utility function

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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