Empirical profile Bayesian estimation for extrapolation of historical adult data to pediatric drug development

Use of transportability methods for real-world evidence generation: a review of current applications

Aim: To evaluate how transportability methods are currently used for real-world evidence (RWE) generation to inform good practices and support adoption and acceptance of these methods in the RWE context. Methods: We conducted a targeted literature review to identify studies that transported an effect estimate of the clinical effectiveness or safety of a biomedical exposure to a target real-world population. Records were identified from PubMed-indexed articles published any time before 25 July 2023 (inclusive). Two reviewers screened abstracts/titles and reviewed the full text of candidate studies to identify the final set of articles. Data on the therapeutic area, exposure(s), outcome(s), original and target populations and details of the transportability analysis (e.g., analytic method used, estimate transported, stated assumptions) were abstracted from each article. Results: Of 458 unique records identified, six were retained in the final review. Articles were published during 2021-2023, focused on the US/Canada context, and covered a range of therapeutic areas. Four studies transported an RCT effect estimate, while two transported effect estimates derived from real-world data. Almost all articles used weighting methods to transport estimates. Two studies discussed all transportability assumptions, and one evaluated the likelihood of meeting all assumptions and the impact of potential violations. Conclusion: The use of transportability methods for RWE generation is an emerging and promising area of research to address evidence gaps in settings with limited data and infrastructure. More transparent and rigorous reporting of methods, assumptions and limitations may increase the use and acceptability of transportability for producing robust evidence on treatment effectiveness and safety.

Bayesian Design of Superiority Trials: Methods and Applications

In this paper, we lay out the basic elements of Bayesian sample size determination (SSD) for the Bayesian design of a two-arm superiority clinical trial. We develop a flowchart of the Bayesian SSD that highlights the critical components of a Bayesian design and provides a practically useful roadmap for designing a Bayesian clinical trial in real world applications. We empirically examine the amount of borrowing, the choice of noninformative priors, and the impact of model misspecification on the Bayesian type I error and power. A formal and statistically rigorous formulation of conditional borrowing within the decision rule framework is developed. Moreover, by extending the partial borrowing power priors, a new borrowing-by-parts power prior for incorporating historical data is proposed. Computational algorithms are also developed to calculate the Bayesian type I error and power. Extensive simulation studies are carried out to explore the operating characteristics of the proposed Bayesian design of a superiority trial.