ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2017
A Calibrated Power Prior Approach to Borrow Information from Historical Data with Application to Biosimilar Clinical Trials.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Novel clinical trial design and analytic methods to tackle challenges in therapeutic development in rare diseases.Annals of translational medicine · 2022Trial
- Historical Measurement Information Can Be Used to Improve Estimation of Structural Parameters in Structural Equation Models With Small Samples.Educational and psychological measurement · 2025Article
- The Cox Model With Adaptive Fused Group Bridge Penalty to Incorporate Historical Data Into the Analysis of Clinical Trials With an Application to BMT CTN 1101.Statistics in medicine · 2025Article
- Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift.Statistics in biopharmaceutical research · 2024Article
- Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies.Medical review (2021) · 2023Article
- Elastic priors to dynamically borrow information from historical data in clinical trials.Biometrics · 2023Article
- The scale transformed power prior for use with historical data from a different outcome model.Statistics in medicine · 2023Article
- BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.Statistics in medicine · 2022Article
- A Bayesian group sequential design for randomized biosimilar clinical trials with adaptive information borrowing from historical data.Journal of biopharmaceutical statistics · 2022Article
- Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs.BMC medical research methodology · 2022Article
- Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials.JCO precision oncology · 2022Review
- Bayesian Design of Superiority Trials: Methods and Applications.Statistics in biopharmaceutical research · 2022Article
- A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods.Pharmaceutical statistics · 2021Article
- An adaptive power prior for sequential clinical trials - Application to bridging studies.Statistical methods in medical research · 2020Article
- Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.Biometrics · 2020Article
- A phase I dose-finding design with incorporation of historical information and adaptive shrinking boundaries.PloS one · 2020Article
- Bayesian clinical trial design using historical data that inform the treatment effect.Biostatistics (Oxford, England) · 2019Article
- Control of Unconditional Type I Error in Clinical Trials With External Control Borrowing-A Two-Stage Adaptive Design Perspective.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A biosimilar refers to a follow-on biologic intended to be approved for marketing based on biosimilarity to an existing patented biological product (i.e., the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time a biosimilar product is developed, we propose the calibrated power prior, which allows our design to adaptively borrow information from the historical data according to the congruence between the historical data and the new data collected from the current trial. We propose a new measure, the Bayesian biosimilarity index, to measure the similarity between the biosimilar and the reference product. During the trial, we evaluate the Bayesian biosimilarity index in a group sequential fashion based on the accumulating interim data, and stop the trial early once there is enough information to conclude or reject the similarity. Extensive simulation studies show that the proposed design has higher power than traditional designs. We applied the proposed design to a biosimilar trial for treating rheumatoid arthritis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.