Bayesian design of biosimilars clinical programs involving multiple therapeutic indications

Autor: Jean Pan, Matthew A. Psioda, Joseph G. Ibrahim, Kuolung Hu, Yang Zhang
Rok vydání: 2018
Předmět:
Statistics and Probability
Biometry
Computer science
Endpoint Determination
Bayesian probability
Context (language use)
Machine learning
computer.software_genre
01 natural sciences
Data type
General Biochemistry
Genetics and Molecular Biology
Article
Arthritis
Rheumatoid
010104 statistics & probability
03 medical and health sciences
Humans
Computer Simulation
0101 mathematics
Equivalence (measure theory)
Biosimilar Pharmaceuticals
Lymphoma
Follicular
030304 developmental biology
Hyperparameter
0303 health sciences
Clinical Trials as Topic
Models
Statistical
General Immunology and Microbiology
business.industry
Applied Mathematics
Biosimilar
Bayes Theorem
General Medicine
Therapeutic Equivalency
Sample size determination
Sample Size
Multivariate Analysis
Linear Models
Artificial intelligence
General Agricultural and Biological Sciences
business
computer
Type I and type II errors
Zdroj: Biometrics
ISSN: 1541-0420
Popis: 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.
Databáze: OpenAIRE
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