Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data

Utilization of historical data is increasingly common for gaining efficiency in the drug development and decision-making processes. The underlying issue of between-trial heterogeneity in clinical trials is a barrier in making these methods standard practice in the pharmaceutical industry. Common methods for historical borrowing discount the borrowed information based on the similarity between outcomes in the historical and current data. However, individual clinical trials and their outcomes are intrinsically heterogenous due to differences in study design, patient characteristics, and changes in standard of care. Additionally, differences in covariate distributions can produce inconsistencies in clinical outcome data between historical and current data when there may be a consistent covariate effect. In such scenario, borrowing historical data is still advantageous even though the population level outcome summaries are different. In this paper, we propose a covariate adjusted meta-analytic-predictive (CA-MAP) prior for historical control borrowing. A MAP prior is assigned to each covariate effect, allowing the amount of borrowing to be determined by the consistency of the covariate effects across the current and historical data. This approach integrates between-trial heterogeneity with covariate level heterogeneity to tune the amount of information borrowed. Our method is unique as it directly models the covariate effects instead of using the covariates to select a similar population to borrow from. In summary, our proposed patient-level extension of the MAP prior allows for the amount of historical control borrowing to depend on the similarity of covariate effects rather than similarity in clinical outcomes.

Bayesian borrowing from historical control data in a vaccine efficacy trial

In the context of vaccine efficacy trial where the incidence rate is very low and a very large sample size is usually expected, incorporating historical data into a new trial is extremely attractive to reduce sample size and increase estimation precision. Nevertheless, for some infectious diseases, seasonal change in incidence rates poses a huge challenge in borrowing historical data and a critical question is how to properly take advantage of historical data borrowing with acceptable tolerance to between-trials heterogeneity commonly from seasonal disease transmission. In this article, we extend a probability-based power prior which determines the amount of information to be borrowed based on the agreement between the historical and current data, to make it applicable for either a single or multiple historical trials available, with constraint on the amount of historical information to be borrowed. Simulations are conducted to compare the performance of the proposed method with other methods including modified power prior (MPP), meta-analytic-predictive (MAP) prior and the commensurate prior methods. Furthermore, we illustrate the application of the proposed method for trial design in a practical setting.

Revisit of test-then-pool methods and some practical considerations

Test-then-pool is a simple statistical method that borrows historical information to improve efficiency of the drug development process. The original test-then-pool method examines the difference between the historical and current information and then pools the information if there is no significant difference. One drawback of this method is that a nonsignificant difference may not always imply consistency between the historical and current information. As a result, the original test-then-pool method is more likely to incorrectly borrow information from the historical control when the current trial has a small sample size. Statistically, it is more natural to use an equivalence test for examining the consistency. This manuscript develops an equivalence-based test-then-pool method for a continuous endpoint, explains the relationship between the two test-then-pool methods, explores the choice of an equivalence margin through the overlap probability, and proposes an adjustment to the nominal testing level for controlling type I error under the true consistency scenario. Furthermore, the analytical forms of the type I error and power for the two test-then-pool methods are derived, and practical considerations for using them are presented.