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A Bayesian framework to evaluate non-inferiority in randomized controlled trials of uncommon conditions
Victoria R Cornelius1, Jack Elkes1, Ian R White2
1Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK.
Background And Objectives:
Noninferiority (NI) trials typically require larger sample sizes than superiority comparisons. This is problematic for uncommon conditions where recruitment is restricted. When a power calculation results in an unfeasibly large sample size, there is a need to justify whether a trial in an uncommon condition is worth undertaking. We propose reversing the question to demonstrate what can be shown with a feasible maximum sample size using a Bayesian framework in place of a traditional power calculation.
Methods:
We propose using the posterior probability of noninferiority to demonstrate the value of undertaking an NI trial using a credible NI margin. We describe a five-part Bayesian framework: the data generation/analysis model; maximum feasible sample size; different potential outcomes; primary NI margin; and plausible priors. We illustrate the framework in an NIHR-funded NI trial of mepolizumab compared to omalizumab in children with severe therapy resistant asthma. We examine the trial operating characteristics when mepolizumab is inferior, identical, and superior to omalizumab under four differing prior distributions on the treatment effect (3 informative, 1 vague) and demonstrate suitable interpretation.
Results:
Our case study had a maximum feasible sample size of 150 severe therapy resistant asthmatic children. Using the proposed Bayesian framework we demonstrated that if mepolizumab was truly identical or superior to omalizumab, then the average posterior probability of noninferiority would be reassuringly high, from 0.87 to >0.99. The probabilities were reassuringly low when mepolizumab was inferior (≤0.22). The framework provided a comprehensible summary for reviewers to judge the value of undertaking the trial.
Conclusion:
A Bayesian approach using posterior probability for NI trials can offer a practical way to assess the value of undertaking a trial in an uncommon condition with a fixed sample size as an alternative to a power calculation.
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