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.
Highlights
Randomised controlled trials (RCTs) are considered the gold standard approach to learn about the relative efficacy of competing treatment options for evidence-based patient care
Our results suggest that correctly modelling a time trend and adjusting for separation via Firth correction can safeguard the validity of trial analyses using Response-adaptive randomisation (RAR), that is, by maintaining correct type I error rates and delivering a level of statistical power similar to that obtainable when no trend is present
These results fail to illustrate the learning-earning trade-off that characterises the choice between a complete randomisation (CR) and a RAR procedure and the reasons why the forward-looking Gittins index rule” (FLGI) could be desirable to use from a patient benefit perspective
Summary
Randomised controlled trials (RCTs) are considered the gold standard approach to learn about the relative efficacy of competing treatment options for evidence-based patient care. Karrison et al[5] investigate the type I error inflation induced by various RAR rules implemented within a 2-armed group sequential design with a binary outcome in which, depending on the observed value of the corresponding z-statistics, the group of patients is allocated in one of 4 possible fixed ratios R(z). If the possibility of a large drift occurring during the trial is a concern and a RAR scheme is being considered for designing such a trial, subsequent and related questions are as follows: Do any “robust” hypothesis testing procedures exist that naturally preserve type I error in the presence of an unknown time trend?
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