|Title||Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Psioda, Matthew A., Kuolung Hu, Yang Zhang, Jean Pan, and Joseph G. Ibrahim|
|Date Published||2020 Jun|
|Keywords||Arthritis, Rheumatoid, Bayes Theorem, Biometry, Biosimilar Pharmaceuticals, Clinical Trials as Topic, Computer Simulation, Endpoint Determination, Humans, Linear Models, Lymphoma, Follicular, Models, Statistical, Multivariate Analysis, Sample Size, Therapeutic Equivalency|
In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.
|Original Publication||Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.|
|PubMed Central ID||PMC7170751|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.