Bayesian analysis in confirmatory clinical trials: A narrative review and discussion of current practice

Bayesian methods allow trial investigators to combine evidence obtained within a clinical trial with relevant evidence that is available outside the trial. Bayesian analyses are now widely used in the drug development process, to inform internal 'go/no-go' decisions about planned studies, for example when deciding whether a drug should proceed from phase II to a phase III trial. However, Bayesian analyses are not commonly used for analysis of phase III (confirmatory) trials.

In this article, we performed a narrative review of confirmatory trials using Bayesian methods for their primary analysis, to explore which types of trials chose Bayesian methods, why they chose a Bayesian analysis and how the methods were used. We reviewed published papers over a 6-year period and explored the characteristics of trials using Bayesian methods for their primary analysis, their reasons for choosing a Bayesian analysis, whether any informative priors were used and if so how they were informed. Next, we selected four trials from the review as case studies and presented their motivation for using Bayesian methods and their Bayesian analyses in more detail.

Our narrative review found that the number of Bayesian methods in confirmatory clinical trials has approximately doubled over the past 6 years, reflecting growing familiarity among investigators. Ninety-four papers were eligible for inclusion, presenting results from 69 separate trials. The most common reason given for choosing Bayesian methods was to make direct probability statements about the superiority and/or futility of the interventions evaluated; this was mentioned for 49% of trials. Flexibility in adapting the design or use of Bayesian stopping rules was another very common motivation, cited for 47% of trials. Borrowing information through informative priors was cited for a much smaller proportion (16%) of trials. The majority of trials (75%) specified vague or weakly informative priors for all parameters.

Among the reasons given for choosing Bayesian methods, we consider the use of informative priors or making direct probability statements to be the strongest motivations for a Bayesian analysis, because there are no equivalent frequentist approaches. Making direct probability statements was the most common motivation provided, while informative priors were not often used. In settings with recruitment difficulties, we recommend considering borrowing relevant information, to gain power and precision. In all confirmatory trial settings, we recommend that Bayesian approaches are used only with careful justification, investigators make clear whether the methods and priors were pre-planned, and alternative frequentist approaches are considered.