Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes

Anna Heath, Martin Offringa, Petros Pechlivanoglou, Juan David Rios, Terry P Klassen, Naveen Poonai, Eleanor Pullenayegum, KidsCAN PERC Innovative Paediatric Clinical Trials Team, Anna Heath, Martin Offringa, Petros Pechlivanoglou, Juan David Rios, Terry P Klassen, Naveen Poonai, Eleanor Pullenayegum, KidsCAN PERC Innovative Paediatric Clinical Trials Team

Abstract

Background/aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the "inferior" intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials.

Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes.

Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%.

Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims.

Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016.

Keywords: Bayesian predictive power; DSMB, Data Safety Monitoring Board; IN, Intranasal; INK Trial, Intranasal Ketamine for Procedural Sedation trial; IV, Intravenous; Non-inferiority trial; Procedural sedation; Stopping rule; Trial design.

Conflict of interest statement

We have no conflicting interests to declare.

© 2020 The Authors.

Figures

Fig. 1
Fig. 1
The value of test statistic T plotted against the probability of observing T at the boundary of the null hypothesis. The bold dots (shown in red) represent values of T that are in the rejection region, i.e., the values of T that are less than t∗=0.115. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2
Fig. 2
The probability of rejecting the null hypothesis upon completion of the INK trial for different combination of failures on the active control (x-axis) and novel treatment (y-axis) at the interim analysis of the INK trial, proposed after an enrollment of 70 participants in each trial arm.

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