Pharmacometrics enhanced Bayesian borrowing approach to improve clinical trial efficiency: Case of empagliflozin in type 2 diabetes
Abstract
We report use of a pharmacometrics enhanced Bayesian borrowing (PEBB) approach to leverage historical clinical trial data on a drug product to build models, project the outcome of future clinical trials, and borrow information from these projections to improve the efficiency of future target trials. This design takes a two‐stage approach. First, a design phase is performed before target trial data are available to determine the operating characteristics and an appropriate tuning parameter that will be used in the subsequent analysis phase of a chosen target trial. Second, once the target trial data are available, the analysis phase is performed with the determined tuning parameter. This step is where borrowing is applied from these projections to inform the results for the target trial. To illustrate how a PEBB could improve the efficiency of clinical trials, we apply our design to trials with empagliflozin for treating patients with type 2 diabetes. We performed a retrospective evaluation applying the method to a phase III target trial and a hypothetical smaller trial. The type I error could be kept below 10% while increasing the trial power and effective sample size. Our findings suggest that a PEBB has the potential to increase the power of clinical trials, while controlling for type I error, by leveraging the information from previous trials through population pharmacokinetic/pharmacodynamic modeling and simulation.
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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