Unit information prior for adaptive information borrowing from multiple historical datasets

Bayesian adaptive design for covariate-adaptive historical control information borrowing

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Enhancing generalizability and efficiency in clinical trials through dynamic information borrowing for both experimental and control arms: A simulation study

AIM

Utilizing external information in clinical trials enhances validity by including a wider population and expedites the implementation of adaptive designs, ultimately improving research efficiency. However, current research focused on scenarios in which only the control group benefited from the utilization of external information, while trials involving external information in both experimental and control arms were more complex and might pose challenges when applied in real-world settings.

METHODS

To address these concerns, our study pioneered the application of test-then-pool, normalized power prior, calibrated power prior, and elastic prior to a two-arm information borrowing framework and systematically compared their operating characteristics through a series of simulation studies under most and least desirable scenarios.

RESULTS

In the most desirable scenarios of information borrowing, all methods managed to control the mean of type I error rates within 5%, among which the normalized power prior, calibrated power prior and elastic prior approaches increased the mean of power from 85.94% to 95%. In the least desirable scenarios, the mean type I error rates for normalized power prior, calibrated power prior and elastic prior approaches exceeded 20%, while the mean power decreased to around 80%.

CONCLUSIONS

Our findings reveal that the normalized power prior, calibrated power prior and elastic prior approaches are suitable for situations with minimal heterogeneity between historical and current data, whereas the test-then-pool approach emerges as a more prudent choice when facing substantial discrepancies between historical and current information for trials consider information borrow in both arms.

Unit information prior for incorporating real-world evidence into randomized controlled trials

Randomized controlled trials (RCTs) have been widely recognized as the gold standard to infer the treatment effect in clinical research. Recently, there has been growing interest in enhancing and complementing the result in an RCT by integrating real-world evidence from observational studies. The unit information prior (UIP) is a newly proposed technique that can effectively borrow information from multiple historical datasets. We extend this generic approach to synthesize the non-randomized evidence into a current RCT. Not only does the UIP only require summary statistics published from observational studies for ease of implementation, but it also has clear interpretations and can alleviate the potential bias in the real-world evidence via weighting schemes. Extensive numerical experiments show that the UIP can improve the statistical efficiency in estimating the treatment effect for various types of outcome variables. The practical potential of our UIP approach is further illustrated with a real trial of hydroxychloroquine for treating COVID-19 patients.

A group-sequential randomized trial design utilizing supplemental trial data

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.