Response-adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Sofía S. Villar*, Jack Bowden, James Wason

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)
243 Downloads (Pure)

Abstract

Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2-armed and the multi-armed case. We further propose a RAR design for multi-armed clinical trials, which is computationally efficient and robust to several time trends considered.

Original languageEnglish
Pages (from-to)182-197
Number of pages16
JournalPharmaceutical Statistics
Volume17
Issue number2
Early online date19 Dec 2017
DOIs
Publication statusPublished - 1 Mar 2018

Keywords

  • clinical trials
  • power
  • randomisation test
  • response-adaptive randomisation
  • type I error

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